Mobile Edge Computing: Survey and Research Outlook
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paper_title: The Tactile Internet: Applications and Challenges

paper_content:

Wireless communications today enables us to connect devices and people for an unprecedented exchange of multimedia and data content. The data rates of wireless communications continue to increase, mainly driven by innovation in electronics. Once the latency of communication systems becomes low enough to enable a round-trip delay from terminals through the network back to terminals of approximately 1 ms, an overlooked breakthrough?human tactile to visual feedback control?will change how humans communicate around the world. Using these controls, wireless communications can be the platform for enabling the control and direction of real and virtual objects in many situations of our life. Almost no area of the economy will be left untouched, as this new technology will change health care, mobility, education, manufacturing, smart grids, and much more. The Tactile Internet will become a driver for economic growth and innovation and will help bring a new level of sophistication to societies.
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paper_title: Cloud computing: state-of-the-art and research challenges

paper_content:

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.
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paper_title: Fog and IoT: An Overview of Research Opportunities

paper_content:

Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.
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paper_title: Above the Clouds: A Berkeley View of Cloud Computing

paper_content:

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.
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paper_title: eyeDentify: Multimedia Cyber Foraging from a Smartphone

paper_content:

The recent introduction of smartphones has resulted in an explosion of innovative mobile applications. The computational requirements of many of these applications, however, can not be met by the smartphone itself. The compute power of the smartphone can be enhanced by distributing the application over other compute resources. Existing solutions comprise of a light weight client running on the smartphone and a heavy weight compute server running on, for example, a cloud. This places the user in a dependent position, however, because the user only controls the client application. In this paper, we follow a different model, called cyber foraging, that gives users full control over all parts of the application. We have implemented the model using the Ibis middleware. We evaluate the model using an innovative application in the domain of multimedia computing, and show that cyber foraging increases the application's responsiveness and accuracy whilst decreasing its energy usage.
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paper_title: Accurate, Low-Energy Trajectory Mapping for Mobile Devices

paper_content:

CTrack is an energy-efficient system for trajectory mapping using raw position tracks obtained largely from cellular base station fingerprints. Trajectory mapping, which involves taking a sequence of raw position samples and producing the most likely path followed by the user, is an important component in many location-based services including crowd-sourced traffic monitoring, navigation and routing, and personalized trip management. Using only cellular (GSM) fingerprints instead of power-hungry GPS and WiFi radios, the marginal energy consumed for trajectory mapping is zero. This approach is non-trivial because we need to process streams of highly inaccurate GSM localization samples (average error of over 175 meters) and produce an accurate trajectory. CTrack meets this challenge using a novel two-pass Hidden Markov Model that sequences cellular GSM fingerprints directly without converting them to geographic coordinates, and fuses data from low-energy sensors available on most commodity smart-phones, including accelerometers (to detect movement) and magnetic compasses (to detect turns). We have implemented CTrack on the Android platform, and evaluated it on 126 hours (1,074 miles) of real driving traces in an urban environment. We find that CTrack can retrieve over 75% of a user's drive accurately in the median. An important by-product of CTrack is that even devices with no GPS or WiFi (constituting a significant fraction of today's phones) can contribute and benefit from accurate position data.
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paper_title: Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing

paper_content:

Creating context-aware ad hoc collaborative systems remains to be one of the primary hurdles hampering the ubiquitous deployment of IT and communication services. Especially under mission-critical scenarios, these services must often adhere to strict timing deadlines. We believe empowering such realtime collaboration systems requires context-aware application platforms working in conjunction with ultra-low latency data transmissions. In this paper, we make a strong case that this could be accomplished by combining the novel communication architectures being proposed for 5G with the principles of Mobile Edge Computing (MEC). We show that combining 5G with MEC would enable inter- and intra-domain use cases that are otherwise not feasible.
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paper_title: Cloud computing: state-of-the-art and research challenges

paper_content:

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.
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paper_title: Fog and IoT: An Overview of Research Opportunities

paper_content:

Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.
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paper_title: From Augmented Reality to Augmented Computing: A Look at Cloud-Mobile Convergence

paper_content:

There has been considerable number of virtual and augmented reality applications designed and developed for mobile devices. However the state-of-the-art systems are commonly confined by several limitations. In this position paper the concept ”Cloud-Mobile Convergence for Virtual Reality (CMCVR)” is presented. CMCVR envisions effective and user-friendly integration of the mobile device and cloud-based resources. Through the proposed framework, mobile devices could be augmented to deliver some user experiences comparable to those offered by fixed systems. A preliminary research that follows the CMCVR paradigm is also described.
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paper_title: Fog computing and its role in the internet of things

paper_content:

Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
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paper_title: Above the Clouds: A Berkeley View of Cloud Computing

paper_content:

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.
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paper_title: A Survey of Fog Computing: Concepts, Applications and Issues

paper_content:

Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.
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paper_title: A System Architecture for Context-Aware Mobile Computing

paper_content:

Computer applications traditionally expect a static execution environment. However, this precondition is generally not possible for mobile systems, where the world around an application is constantly changing. This thesis explores how to support and also exploit the dynamic configurations and social settings characteristic of mobile systems. More specifically, it advances the following goals: (1) enabling seamless interaction across devices; (2) creating physical spaces that are responsive to users; and (3) and building applications that are aware of the context of their use. Examples of these goals are: continuing in your office a program started at home; using a PDA to control someone else's windowing UI; automatically canceling phone forwarding upon return to your office; having an airport overhead-display highlight the flight information viewers are likely to be interested in; easily locating and using the nearest printer or fax machine; and automatically turning off a PDA's audible e-mail notification when in a meeting. ::: The contribution of this thesis is an architecture to support context-aware computing; that is, application adaptation triggered by such things as the location of use, the collection of nearby people, the presence of accessible devices and other kinds of objects, as well as changes to all these things over time. Three key issues are addressed: (1) the information needs of applications, (2) where applications get various pieces of information and (3) how information can be efficiently distributed. ::: A dynamic environment communication model is introduced as a general mechanism for quickly and efficiently learning about changes occurring in the environment in a fault tolerant manner. For purposes of scalability, multiple dynamic environment servers store user, device, and, for each geographic region, context information. In order to efficiently disseminate information from these components to applications, a dynamic collection of multicast groups is employed. The thesis also describes a demonstration system based on the Xerox PARCTAB, a wireless palmtop computer.
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paper_title: Offloading Guidelines for Augmented Reality Applications on Wearable Devices

paper_content:

As Augmented Reality (AR) gets popular on wearable devices such as Google Glass, various AR applications have been developed by leveraging synergetic benefits beyond the single technologies. However, the poor computational capability and limited power capacity of current wearable devices degrade runtime performance and sustainability. Computational offloading strategy has been proposed to outsource computation to remote cloud for improving performance. Nevertheless, comparing with mobile devices, the wearable devices have their specific limitations, which induce additional problems and require new thoughts of computational offloading. In this paper, we propose several guidelines of computational offloading for AR applications on wearable devices based on our practical experiences of designing and developing AR applications on Google Glass. The guidelines have been adopted and proved by our application prototypes.
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paper_title: Security and privacy in mobile cloud computing

paper_content:

With the development of cloud computing and mobility, mobile cloud computing has emerged and become a focus of research. By the means of on-demand self-service and extendibility, it can offer the infrastructure, platform, and software services in a cloud to mobile users through the mobile network. Security and privacy are the key issues for mobile cloud computing applications, and still face some enormous challenges. In order to facilitate this emerging domain, we firstly in brief review the advantages and system model of mobile cloud computing, and then pay attention to the security and privacy in the mobile cloud computing. By deeply analyzing the security and privacy issues from three aspects: mobile terminal, mobile network and cloud, we give the current security and privacy approaches.
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paper_title: What Will 5G Be?

paper_content:

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
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paper_title: The Promise of Edge Computing

paper_content:

The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud. Edge computing could address concerns such as latency, mobile devices' limited battery life, bandwidth costs, security, and privacy.
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paper_title: Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

paper_content:

Mobile-Edge computing (MEC) is an emerging technology currently recognized as a key enabler for 5G networks. Compatible with current 4G networks, MEC will address many key uses of the 5G system, motivated by the massive diffusion of the Internet of Things (IoT). This article aims to provide a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the European Telecommunications Standards Institute (ETSI) MEC Industry Specification Group (ISG) standardization organization. We provide some examples of MEC deployment, with special reference to IoT cases, since IoT is recognized as a main driver for 5G. Finally, we discuss the main benefits and challenges for MEC moving toward 5G.
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paper_title: A survey on mobile edge computing

paper_content:

Mobile Edge Computing is an emerging technology that provides cloud and IT services within the close proximity of mobile subscribers. Traditional telecom network operators perform traffic control flow (forwarding and filtering of packets), but in Mobile Edge Computing, cloud servers are also deployed in each base station. Therefore, network operator has a great responsibility in serving mobile subscribers. Mobile Edge Computing platform reduces network latency by enabling computation and storage capacity at the edge network. It also enables application developers and content providers to serve context-aware services (such as collaborative computing) by using real time radio access network information. Mobile and Internet of Things devices perform computation offloading for compute intensive applications, such as image processing, mobile gaming, to leverage the Mobile Edge Computing services. In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed. Later on, a state-of-the-art research efforts on Mobile Edge Computing domain is presented. The paper also presents taxonomy of Mobile Edge Computing, describing key attributes. Finally, open research challenges in successful deployment of Mobile Edge Computing are identified and discussed.
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paper_title: Mobile Edge Computing: A Taxonomy

paper_content:

Mobile Edge Computing proposes co-locating computing and storage resources at base stations of cellular networks. It is seen as a promising technique to alleviate utilization of the mobile core and to reduce latency for mobile end users. Due to the fact that Mobile Edge Computing is a novel approach not yet deployed in real-life networks, recent work discusses merely general and non-technical ideas and concepts. This paper introduces a taxonomy for Mobile Edge Computing applications and analyzes chances and limitations from a technical point of view. Application types which profit from edge deployment are identified and discussed. Furthermore, these applications are systematically classified based on technical metrics.
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paper_title: Edge computing enabling the Internet of Things

paper_content:

Mobile Edge Computing (MEC), a new concept that emerged about a year ago, integrating the IT and the Telecom worlds will have a great impact on the openness of the Telecom market. Furthermore, the virtualization revolution that has enabled the Cloud computing success will benefit the Telecom domain, which in turn will be able to support the IaaS (Infrastructure as a Service). The main objective of MEC solution is the export of some Cloud capabilities to the user's proximity decreasing the latency, augmenting the available bandwidth and decreasing the load on the core network. On the other hand, the Internet of Things (IoT), the Internet of the future, has benefited from the proliferation in the mobile phones' usage. Many mobile applications have been developed to connect a world of things (wearables, home automation systems, sensors, RFID tags etc.) to the Internet. Even if it is not a complete solution for a scalable IoT architecture but the time sensitive IoT applications (e-healthcare, real time monitoring, etc.) will profit from the MEC architecture. Furthermore, IoT can extend this paradigm to other areas (e.g. Vehicular Ad-hoc NETworks) with the use of Software Defined Network (SDN) orchestration to cope with the challenges hindering the IoT real deployment, as we will illustrate in this paper.
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paper_title: Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges

paper_content:

MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.
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paper_title: A Survey of Mobile Cloud Computing Application Models

paper_content:

Smart phones are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. This poses a challenge because smart phones are resource-constrained devices with limited computation power, memory, storage, and energy. Fortunately, the cloud computing technology offers virtually unlimited dynamic resources for computation, storage, and service provision. Therefore, researchers envision extending cloud computing services to mobile devices to overcome the smartphones constraints. The challenge in doing so is that the traditional smartphone application models do not support the development of applications that can incorporate cloud computing features and requires specialized mobile cloud application models. This article presents mobile cloud architecture, offloading decision affecting entities, application models classification, the latest mobile cloud application models, their critical analysis and future research directions.
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paper_title: Energy efficiency of mobile clients in cloud computing

paper_content:

Energy efficiency is a fundamental consideration for mobile devices. Cloud computing has the potential to save mobile client energy but the savings from offloading the computation need to exceed the energy cost of the additional communication. ::: ::: In this paper we provide an analysis of the critical factors affecting the energy consumption of mobile clients in cloud computing. Further, we present our measurements about the central characteristics of contemporary mobile handheld devices that define the basic balance between local and remote computing. We also describe a concrete example, which demonstrates energy savings. ::: ::: We show that the trade-offs are highly sensitive to the exact characteristics of the workload, data communication patterns and technologies used, and discuss the implications for the design and engineering of energy efficient mobile cloud computing solutions.
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paper_title: Optimal Joint Scheduling and Cloud Offloading for Mobile Applications

paper_content:

Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes.
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paper_title: Energy-efficient soft real-time CPU scheduling for mobile multimedia systems

paper_content:

This paper presents GRACE-OS, an energy-efficient soft real-time CPU scheduler for mobile devices that primarily run multimedia applications. The major goal of GRACE-OS is to support application quality of service and save energy. To achieve this goal, GRACE-OS integrates dynamic voltage scaling into soft real-time scheduling and decides how fast to execute applications in addition to when and how long to execute them. GRACE-OS makes such scheduling decisions based on the probability distribution of application cycle demands, and obtains the demand distribution via online profiling and estimation. We have implemented GRACE-OS in the Linux kernel and evaluated it on an HP laptop with a variable-speed CPU and multimedia codecs. Our experimental results show that (1) the demand distribution of the studied codecs is stable or changes smoothly. This stability implies that it is feasible to perform stochastic scheduling and voltage scaling with low overhead; (2) GRACE-OS delivers soft performance guarantees by bounding the deadline miss ratio under application-specific requirements; and (3) GRACE-OS reduces CPU idle time and spends more busy time in lower-power speeds. Our measurement indicates that compared to deterministic scheduling and voltage scaling, GRACE-OS saves energy by 7% to 72% while delivering statistical performance guarantees.
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paper_title: Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing

paper_content:

2014 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2014, Toronto, ON, 27 April-2 May 2014
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paper_title: Computation offloading decisions for reducing completion time

paper_content:

We analyze the conditions in which offloading computation reduces completion time. We extend the existing literature by deriving an inequality (Eq. 4) that relates computation offloading system parameters to the bits per instruction ratio of a computational job. This ratio is the inverse of the arithmetic intensity. We then discuss how this inequality can be used to determine the computations that can benefit from offloading as well as the computation offloading systems required to make offloading beneficial for particular computations.
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paper_title: A Simple Transmit Diversity Technique for Wireless Communications

paper_content:

This paper presents a simple two-branch transmit diversity scheme. Using two transmit antennas and one receive antenna the scheme provides the same diversity order as maximal-ratio receiver combining (MRRC) with one transmit antenna, and two receive antennas. It is also shown that the scheme may easily be generalized to two transmit antennas and receive antennas to provide a diversity order of 2. The new scheme does not require any bandwidth expansion any feedback from the receiver to the transmitter and its computation complexity is similar to MRRC.
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paper_title: Massive MIMO for Next Generation Wireless Systems

paper_content:

Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.
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paper_title: Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems

paper_content:

Millimeter wave (mmWave) communications has been regarded as a key enabling technology for 5G networks, as it offers orders of magnitude greater spectrum than current cellular bands. In contrast to conventional multiple-input–multiple-output (MIMO) systems, precoding in mmWave MIMO cannot be performed entirely at baseband using digital precoders, as only a limited number of signal mixers and analog-to-digital converters can be supported considering their cost and power consumption. As a cost-effective alternative, a hybrid precoding transceiver architecture, combining a digital precoder and an analog precoder, has recently received considerable attention. However, the optimal design of such hybrid precoders has not been fully understood. In this paper, treating the hybrid precoder design as a matrix factorization problem, effective alternating minimization (AltMin) algorithms will be proposed for two different hybrid precoding structures, i.e., the fully-connected and partially-connected structures. In particular, for the fully-connected structure, an AltMin algorithm based on manifold optimization is proposed to approach the performance of the fully digital precoder, which, however, has a high complexity. Thus, a low-complexity AltMin algorithm is then proposed, by enforcing an orthogonal constraint on the digital precoder. Furthermore, for the partially-connected structure, an AltMin algorithm is also developed with the help of semidefinite relaxation. For practical implementation, the proposed AltMin algorithms are further extended to the broadband setting with orthogonal frequency division multiplexing modulation. Simulation results will demonstrate significant performance gains of the proposed AltMin algorithms over existing hybrid precoding algorithms. Moreover, based on the proposed algorithms, simulation comparisons between the two hybrid precoding structures will provide valuable design insights.
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paper_title: Small-Scale Spectrum Aggregation and Sharing

paper_content:

New spectrum bands together with flexible spectrum management are treated as one of the key technical enablers for achievement of the so-called key-performance indicators defined for 5G wireless networks. In this paper, we deal with the small-scale spectrum aggregation and sharing, where a set of even very narrow and disjoint frequency bands closely located on the frequency axis can be utilized simultaneously. We first discuss how such a scheme can be applied to various multicarrier systems, focusing on the non-contiguous orthogonal frequency division multiplexing and non-contiguous filter-bank multicarrier technique. We propose an interference model that takes into account the limitations of both transmitter and receiver frequency selectivity, and apply it to our 5G link-optimization framework, what differentiates our work from other standard approaches to link adaptation. We present the results of hardware experiments to validate assumed theoretical interference models. Finally, we solve the optimization problem subject to the constraints of maximum interference induced to the protected legacy systems (GSM and UMTS). Results confirm that small-scale spectrum aggregation can provide high throughput even when the 5G system operates in a dense heterogeneous network.
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paper_title: Multi-Cell MIMO Cooperative Networks: A New Look at Interference

paper_content:

This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.
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paper_title: Spectrum Refarming: A New Paradigm of Spectrum Sharing for Cellular Networks

paper_content:

Spectrum refarming (SR) refers to a radio resource management technique which supports different generations of cellular networks to operate in the same radio spectrum. In this paper, an underlay SR model is proposed for an Orthogonal Frequency Division Multiple Access (OFDMA) system to share the spectrum of a Code Division Multiple Access (CDMA) system through intelligently exploiting the interference margin provided by the CDMA system when operating with a low system load. The asymptotic signal-to-interference-plus-noise ratio (SINR) of the CDMA system is used to quantify the interference margin, which interestingly does not depend on the instantaneous information (spreading codes and channel state information) of the CDMA system, thanks to its internal power control. By using the transmit power constraints together with the derived interference margin, the uplink resource allocation problem for OFDMA system is formulated and solved through dual decomposition method. The proposed SR system only requires high level system parameters from the CDMA system, hence, the upgrading of legacy CDMA system is not needed. Simulation results have verified our theoretical analysis, and validated the effectiveness of the proposed resource allocation algorithm and its capability to protect the legacy CDMA users.
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paper_title: Femtocells: Past, Present, and Future

paper_content:

Femtocells, despite their name, pose a potentially large disruption to the carefully planned cellular networks that now connect a majority of the planet's citizens to the Internet and with each other. Femtocells - which by the end of 2010 already outnumbered traditional base stations and at the time of publication are being deployed at a rate of about five million a year - both enhance and interfere with this network in ways that are not yet well understood. Will femtocells be crucial for offloading data and video from the creaking traditional network? Or will femtocells prove more trouble than they are worth, undermining decades of careful base station deployment with unpredictable interference while delivering only limited gains? Or possibly neither: are femtocells just a "flash in the pan"; an exciting but short-lived stage of network evolution that will be rendered obsolete by improved WiFi offloading, new backhaul regulations and/or pricing, or other unforeseen technological developments? This tutorial article overviews the history of femtocells, demystifies their key aspects, and provides a preview of the next few years, which the authors believe will see a rapid acceleration towards small cell technology. In the course of the article, we also position and introduce the articles that headline this special issue.
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paper_title: Indoor Millimeter Wave MIMO: Feasibility and Performance

paper_content:

In this paper, we investigate spatial multiplexing at millimeter (mm) wave carrier frequencies for short-range indoor applications by quantifying fundamental limits in line-of-sight (LOS) environments and then investigating performance in the presence of multipath and LOS blockage. Our contributions are summarized as follows. For linear arrays with constrained form factor, an asymptotic analysis based on the properties of prolate spheroidal wave functions shows that a sparse array producing a spatially uncorrelated channel matrix effectively provides the maximum number of spatial degrees of freedom in a LOS environment, although substantial beamforming gains can be obtained by using denser arrays. This motivates our proposed mm-wave MIMO architecture, which utilizes arrays of subarrays to provide both directivity and spatial multiplexing gains. System performance is evaluated in a simulated indoor environment using a ray-tracing model that incorporates multipath effects and potential LOS blockage. Eigenmode transmission with waterfilling power allocation serves as a performance benchmark, and is compared to the simpler scheme of beamsteering transmission with MMSE reception and a fixed signal constellation. Our numerical results provide insight into the spatial variations of attainable capacity within a room, and the combinations of beamsteering and spatial multiplexing used in different scenarios.
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paper_title: Modeling and Analysis of K-Tier Downlink Heterogeneous Cellular Networks

paper_content:

Cellular networks are in a major transition from a carefully planned set of large tower-mounted base-stations (BSs) to an irregular deployment of heterogeneous infrastructure elements that often additionally includes micro, pico, and femtocells, as well as distributed antennas. In this paper, we develop a tractable, flexible, and accurate model for a downlink heterogeneous cellular network (HCN) consisting of K tiers of randomly located BSs, where each tier may differ in terms of average transmit power, supported data rate and BS density. Assuming a mobile user connects to the strongest candidate BS, the resulting Signal-to-Interference-plus-Noise-Ratio (SINR) is greater than 1 when in coverage, Rayleigh fading, we derive an expression for the probability of coverage (equivalently outage) over the entire network under both open and closed access, which assumes a strikingly simple closed-form in the high SINR regime and is accurate down to -4 dB even under weaker assumptions. For external validation, we compare against an actual LTE network (for tier 1) with the other K-1 tiers being modeled as independent Poisson Point Processes. In this case as well, our model is accurate to within 1-2 dB. We also derive the average rate achieved by a randomly located mobile and the average load on each tier of BSs. One interesting observation for interference-limited open access networks is that at a given \sinr, adding more tiers and/or BSs neither increases nor decreases the probability of coverage or outage when all the tiers have the same target-SINR.
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paper_title: User-Centric Intercell Interference Nulling for Downlink Small Cell Networks

paper_content:

Small cell networks are regarded as a promising candidate to meet the exponential growth of mobile data traffic in cellular networks. With a dense deployment of access points, spatial reuse will be improved, and uniform coverage can be provided. However, such performance gains cannot be achieved without effective intercell interference management. In this paper, a novel interference coordination strategy, called user-centric intercell interference nulling , is proposed for small cell networks. A main merit of the proposed strategy is its ability to effectively identify and mitigate the dominant interference for each user. Different from existing works, each user selects the coordinating base stations (BSs) based on the relative distance between the home BS and the interfering BSs, called the interference nulling (IN) range , and thus interference nulling adapts to each user's own interference situation. By adopting a random spatial network model, we derive an approximate expression of the successful transmission probability to the typical user, which is then used to determine the optimal IN range. Simulation results shall confirm the tightness of the approximation, and demonstrate significant performance gains (about 35–40 $\%$ ) of the proposed coordination strategy, compared with the non-coordination case. Moreover, it is shown that the proposed strategy outperforms other interference nulling methods. Finally, the effect of imperfect channel state information (CSI) is investigated, where CSI is assumed to be obtained via limited feedback. It is shown that the proposed coordination strategy still provides significant performance gains even with a moderate number of feedback bits.
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paper_title: Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

paper_content:

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
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paper_title: An analysis of power consumption in a smartphone

paper_content:

Mobile consumer-electronics devices, especially phones, are powered from batteries which are limited in size and therefore capacity. This implies that managing energy well is paramount in such devices. ::: ::: Good energy management requires a good understanding of where and how the energy is used. To this end we present a detailed analysis of the power consumption of a recent mobile phone, the Openmoko Neo Freerunner. We measure not only overall system power, but the exact breakdown of power consumption by the device's main hardware components. We present this power breakdown for micro-benchmarks as well as for a number of realistic usage scenarios. These results are validated by overall power measurements of two other devices: the HTC Dream and Google Nexus One. ::: ::: We develop a power model of the Freerunner device and analyse the energy usage and battery lifetime under a number of usage patterns. We discuss the significance of the power drawn by various components, and identify the most promising areas to focus on for further improvements of power management. We also analyse the energy impact of dynamic voltage and frequency scaling of the device's application processor.
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paper_title: Processor Design for Portable Systems

paper_content:

Processors used in portable systems must provide highly energy-efficient operation, due to the importance of battery weight and size, without compromising high performance when the user requires it. The user-dependent modes of operation of a processor in portable systems are described and separate metrics for energy efficiency for each of them are found to be required. A variety of well known low-power techniques are re-evaluated against these metrics and in some cases are not found to be appropriate leading to a set of energy-efficient design principles. Also, the importance of idle energy reduction and the joint optimization of hardware and software will be examined for achieving the ultimate in low-energy, high-performance design.
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paper_title: The Energy/Frequency Convexity Rule: Modeling and Experimental Validation on Mobile Devices

paper_content:

This paper provides both theoretical and experimental evidence for the existence of an Energy/Frequency Convexity Rule, which relates energy consumption and CPU frequency on mobile devices. We monitored a typical smartphone running a specific computing-intensive kernel of multiple nested loops written in C using a high-resolution power gauge. Data gathered during a week-long acquisition campaign suggest that energy consumed per input element is strongly correlated with CPU frequency, and, more interestingly, the curve exhibits a clear minimum over a 0.2 GHz to 1.6 GHz window. We provide and motivate an analytical model for this behavior, which fits well with the data. Our work should be of clear interest to researchers focusing on energy usage and minimization for mobile devices, and provide new insights for optimization opportunities.
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paper_title: Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

paper_content:

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
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paper_title: Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing

paper_content:

Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios.
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paper_title: Joint allocation of computation and communication resources in multiuser mobile cloud computing

paper_content:

Mobile cloud computing is offering a very powerful storage and computational facility to enhance the capabilities of resource-constrained mobile handsets. However, full exploitation of the cloud computing capabilities can be achieved only if the allocation of radio and computational capabilities is performed jointly. In this paper, we propose a method to jointly optimize the transmit power, the number of bits per symbol and the CPU cycles assigned to each application in order to minimize the power consumption at the mobile side, under an average latency constraint dictated by the application requirements. We consider the case of a set of mobile handsets served by a single cloud and we show that the optimization leads to a one-to-one relationship between the transmit power and the percentage of CPU cycles assigned to each user. Based on our optimization, we propose then a computation scheduling technique and verify the stability of the computations queue. Then we show how these queues are affected by the degrees of freedom of the channels between mobile handsets and server.
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paper_title: A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems

paper_content:

Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of cloud computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding quality of service (QoS) experienced by users. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated. In this paper, we present an analytical model, based on stochastic reward nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.
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paper_title: Xen and the art of virtualization

paper_content:

Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.
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paper_title: Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

paper_content:

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function , which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.
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paper_title: Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

paper_content:

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.
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paper_title: Modeling of the resource allocation in cloud computing centers

paper_content:

Cloud computing offers on-demand network access to the computing resources through virtualization. This paradigm shifts the computer resources to the cloud, which results in cost savings as the users leasing instead of owning these resources. Clouds will also provide power constrained mobile users accessibility to the computing resources. In this paper, we develop performance models of these systems. We assume that jobs arrive to the system according to a Poisson process and they may have quite general service time distributions. Each job may consist of multiple numbers of tasks with each task requiring a virtual machine (VM) for its execution. The size of a job is determined by the number of its tasks, which may be a constant or a variable. The jobs with variable sizes may generate new tasks during their service times. In the case of constant job size, we allow different classes of jobs, with each class being determined through their arrival and service rates and number of tasks in a job. In the variable case a job generates randomly new tasks during its service time. The latter requires dynamic assignment of VMs to a job, which will be needed in providing service to mobile users. We model the systems with both constant and variable size jobs using birth-death processes. In the case of constant job size, we determined joint probability distribution of the number of jobs from each class in the system, job blocking probabilities and distribution of the utilization of resources for systems with both homogeneous and heterogeneous types of VMs. We have also analyzed tradeoffs for turning idle servers off for power saving. In the case of variable job sizes, we have determined distribution of the number of jobs in the system and average service time of a job for systems with both infinite and finite amount of resources. We have presented numerical results and any approximations are verified by simulation. The results of the paper may be used in the dimensioning of cloud computing centers.
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paper_title: Above the Clouds: A Berkeley View of Cloud Computing

paper_content:

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.
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paper_title: Power provisioning for a warehouse-sized computer

paper_content:

Large-scale Internet services require a computing infrastructure that can beappropriately described as a warehouse-sized computing system. The cost ofbuilding datacenter facilities capable of delivering a given power capacity tosuch a computer can rival the recurring energy consumption costs themselves.Therefore, there are strong economic incentives to operate facilities as closeas possible to maximum capacity, so that the non-recurring facility costs canbe best amortized. That is difficult to achieve in practice because ofuncertainties in equipment power ratings and because power consumption tends tovary significantly with the actual computing activity. Effective powerprovisioning strategies are needed to determine how much computing equipmentcan be safely and efficiently hosted within a given power budget. In this paper we present the aggregate power usage characteristics of largecollections of servers (up to 15 thousand) for different classes ofapplications over a period of approximately six months. Those observationsallow us to evaluate opportunities for maximizing the use of the deployed powercapacity of datacenters, and assess the risks of over-subscribing it. We findthat even in well-tuned applications there is a noticeable gap (7 - 16%)between achieved and theoretical aggregate peak power usage at the clusterlevel (thousands of servers). The gap grows to almost 40% in wholedatacenters. This headroom can be used to deploy additional compute equipmentwithin the same power budget with minimal risk of exceeding it. We use ourmodeling framework to estimate the potential of power management schemes toreduce peak power and energy usage. We find that the opportunities for powerand energy savings are significant, but greater at the cluster-level (thousandsof servers) than at the rack-level (tens). Finally we argue that systems needto be power efficient across the activity range, and not only at peakperformance levels.
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paper_title: Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

paper_content:

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
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paper_title: DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

paper_content:

To cope with increasing energy consumption in mobile devices, the mobile cloud offloading has received considerable attention from its ability to offload processing tasks of mobile devices to cloud servers, and previous studies have focused on single type tasks in fixed network environments. However, real network environments are spatio-temporally varying, and typical mobile devices have not only various types of tasks, e.g., network traffic, cloud offloadable/nonoffloadable workloads but also capabilities of CPU frequency scaling and network interface selection between WiFi and cellular. In this paper, we first jointly consider the following three dynamic problems in real mobile environments: 1) cloud offloading policy, i.e., determining to use local CPU resources or cloud resources; 2) allocation of tasks to transmit through networks and to process in local CPU; and 3) CPU clock speed and network interface controls. We propose a DREAM algorithm by invoking the Lyapunov optimization and mathematically prove that it minimizes CPU and network energy for given delay constraints. Trace-driven simulation based on real measurements demonstrates that DREAM can save over 35% of total energy than existing algorithms with the same delay. We also design DREAM architecture and demonstrate the applicability of DREAM in practice.
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paper_title: Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling

paper_content:

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.
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paper_title: QoE-Aware Computation Offloading Scheduling to Capture Energy-Latency Tradeoff in Mobile Clouds

paper_content:

Computation offloading is a promising application of mobile clouds that can save energy of mobile devices via optimal transmission scheduling of mobile-to-cloud task offloading. Existing approaches to computation offloading have addressed various aspects of the tradeoff between energy consumption and application latency, but none of them explicitly considered the dependency in optimization on the mobile user''s context, e.g., user tendency, the remaining battery level. This paper captures such a user-centric perspective in the energy-latency tradeoff via a quality-of-experience (QoE) based cost function, and formulates the problem of data offloading scheduling as dynamic programming (DP). To derive the optimal schedule, we first introduce a database-assisted optimal DP algorithm and then propose a suboptimal but computationally-efficient approximate DP (ADP) algorithm based on the limited lookahead technique. An extensive numerical analysis has revealed that the ADP algorithm achieves near-optimal performance incurring only 2.27% extra cost on average than the optimum, and enhances QoE by up to 4.46 times compared to the energy-only scheduling.
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paper_title: Inter-Layer Per-Mobile Optimization of Cloud Mobile Computing: A Message-Passing Approach

paper_content:

Cloud mobile computing enables the offloading of computation-intensive applications from a mobile device to a cloud processor via a wireless interface. In light of the strong interplay between offloading decisions at the application layer and physical-layer parameters, which determine the energy and latency associated with the mobile-cloud communication, this paper investigates the inter-layer optimization of fine-grained task offloading across both layers. In prior art, this problem was formulated, under a serial implementation of processing and communication, as a mixed integer program, entailing a complexity that is exponential in the number of tasks. In this work, instead, algorithmic solutions are proposed that leverage the structure of the call graphs of typical applications by means of message passing on the call graph, under both serial and parallel implementations of processing and communication. For call trees, the proposed solutions have a linear complexity in the number of tasks, and efficient extensions are presented for more general call graphs that include "map" and "reduce"-type tasks. Moreover, the proposed schemes are optimal for the serial implementation, and provide principled heuristics for the parallel implementation. Extensive numerical results yield insights into the impact of inter-layer optimization and on the comparison of the two implementations.
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paper_title: Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems

paper_content:

Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.
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paper_title: Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks

paper_content:

Current estimates of mobile data traffic in the years to come foresee a 1,000 increase of mobile data traffic in 2020 with respect to 2010, or, equivalently, a doubling of mobile data traffic every year. This unprecedented growth demands a significant increase of wireless network capacity. Even if the current evolution of fourth-generation (4G) systems and, in particular, the advancements of the long-term evolution (LTE) standardization process foresees a significant capacity improvement with respect to third-generation (3G) systems, the European Telecommunications Standards Institute (ETSI) has established a roadmap toward the fifth-generation (5G) system, with the aim of deploying a commercial system by the year 2020 [1]. The European Project named ?Mobile and Wireless Communications Enablers for the 2020 Information Society? (METIS), launched in 2012, represents one of the first international and large-scale research projects on fifth generation (5G) [2]. In parallel with this unparalleled growth of data traffic, our everyday life experience shows an increasing habit to run a plethora of applications specifically devised for mobile devices, (smartphones, tablets, laptops)for entertainment, health care, business, social networking, traveling, news, etc. However, the spectacular growth in wireless traffic generated by this lifestyle is not matched with a parallel improvement on mobile handsets? batteries, whose lifetime is not improving at the same pace [3]. This determines a widening gap between the energy required to run sophisticated applications and the energy available on the mobile handset. A possible way to overcome this obstacle is to enable the mobile devices, whenever possible and convenient, to offload their most energy-consuming tasks to nearby fixed servers. This strategy has been studied for a long time and is reported in the literature under different names, such as cyberforaging [4] or computation offloading [5], [6]. In recent years, a strong impulse to computation offloading has come through cloud computing (CC), which enables the users to utilize resources on demand. The resources made available by a cloud service provider are: 1) infrastructures, such as network devices, storage, servers, etc., 2) platforms, such as operating systems, offering an integrated environment for developing and testing custom applications, and 3) software, in the form of application programs. These three kinds of services are labeled, respectively, as infrastructure as a service, platform as a service, and software as a service. In particular, one of the key features of CC is virtualization, which makes it possible to run multiple operating systems and multiple applications over the same machine (or set of machines), while guaranteeing isolation and protection of the programs and their data. Through virtualization, the number of virtual machines (VMs) can scale on ?demand, thus improving the overall system computational efficiency. Mobile CC (MCC) is a specific case of CC where the user accesses the cloud services through a mobile handset [5]. The major limitations of today?s MCC are the energy consumption associated to the radio access and the latency experienced in reaching the cloud provider through a wide area network (WAN). Mobile users located at the edge of macrocellular networks are particularly disadvantaged in terms of power consumption and, furthermore, it is very difficult to control latency over a WAN. As pointed out in [7]?[9], humans are acutely sensitive to delay and jitter: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems, in particular in the so-called tactile Internet [10], are quite small (in the order of milliseconds), a strict latency control must be somehow incorporated in near future MCC. Meeting this constraint requires a deep ?rethinking of the overall service chain, from the physical layer up to virtualization.
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paper_title: Optimal Joint Scheduling and Cloud Offloading for Mobile Applications

paper_content:

Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes.
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paper_title: Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing

paper_content:

2014 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2014, Toronto, ON, 27 April-2 May 2014
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paper_title: Hermes: Latency Optimal Task Assignment for Resource-constrained Mobile Computing

paper_content:

With mobile devices increasingly able to connect to cloud servers from anywhere, resource-constrained devices can potentially perform offloading of computational tasks to either save local resource usage or improve performance. It is of interest to find optimal assignments of tasks to local and remote devices that can take into account the application-specific profile, availability of computational resources, and link connectivity, and find a balance between energy consumption costs of mobile devices and latency for delay-sensitive applications. We formulate an NP-hard problem to minimize the application latency while meeting prescribed resource utilization constraints. Different from most of existing works that either rely on the integer programming solver, or on heuristics that offer no theoretical performance guarantees, we propose Hermes, a novel fully polynomial time approximation scheme (FPTAS). We identify for a subset of problem instances, where the application task graphs can be described as serial trees, Hermes provides a solution with latency no more than $(1+\epsilon)$ times of the minimum while incurring complexity that is polynomial in problem size and $\frac{1}{\epsilon}$ . We further propose an online algorithm to learn the unknown dynamic environment and guarantee that the performance gap compared to the optimal strategy is bounded by a logarithmic function with time. Evaluation is done by using real data set collected from several benchmarks, and is shown that Hermes improves the latency by $16$ percent compared to a previously published heuristic and increases CPU computing time by only $0.4$ percent of overall latency.
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paper_title: A Dynamic Offloading Algorithm for Mobile Computing

paper_content:

Offloading is an effective method for extending the lifetime of handheld mobile devices by executing some components of applications remotely (e.g., on the server in a data center or in a cloud). In this article, to achieve energy saving while satisfying given application execution time requirement, we present a dynamic offloading algorithm, which is based on Lyapunov optimization. The algorithm has low complexity to solve the offloading problem (i.e., to determine which software components to execute remotely given available wireless network connectivity). Performance evaluation shows that the proposed algorithm saves more energy than the existing algorithm while meeting the requirement of application execution time.
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paper_title: Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

paper_content:

This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.
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paper_title: Energy Delay Tradeoff in Cloud Offloading for Multi-Core Mobile Devices

paper_content:

Cloud offloading is considered a promising approach for both energy conservation and storage/computation enhancement for resource-limited mobile devices. In this paper, we present a Lyapunov optimization-based scheme for cloud offloading scheduling, as well as download scheduling for cloud execution output, for multiple applications running in a mobile device with a multi-core CPU. We derive an online algorithm and prove performance bounds for the proposed algorithm with respect to average power consumption and average queue length, which is indicative of delay, and reveal the fundamental tradeoff between the two optimization goals. The performance of the proposed online scheduling scheme is validated with trace-driven simulations.
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paper_title: Cloud Offloading for Multi-Radio Enabled Mobile Devices

paper_content:

The advent of 5G networking technologies has increased the expectations from mobile devices, in that, more sophisticated, computationally intense applications are expected to be delivered on the mobile device which are themselves getting smaller and sleeker. This predicates a need for offloading computationally intense parts of the applications to a resource strong cloud. Parallely, in the wireless networking world, the trend has shifted to multi-radio (as opposed to multi-channel) enabled communications. In this paper, we provide a comprehensive computation offloading solution that uses the multiple radio links available for associated data transfer, optimally. Our contributions include: a comprehensive model for the energy consumption from the perspective of the mobile device; the formulation of the joint optimization problem to minimize the energy consumed as well as allocating the associated data transfer optimally through the available radio links and an iterative algorithm that converges to a locally optimal solution. Simulations on an HTC phone, running a 14-component application and using the Amazon EC2 as the cloud, show that the solution obtained through the iterative algorithm consumes only 3% more energy than the optimal solution (obtained via exhaustive search).
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paper_title: Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

paper_content:

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.
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paper_title: Joint allocation of computation and communication resources in multiuser mobile cloud computing

paper_content:

Mobile cloud computing is offering a very powerful storage and computational facility to enhance the capabilities of resource-constrained mobile handsets. However, full exploitation of the cloud computing capabilities can be achieved only if the allocation of radio and computational capabilities is performed jointly. In this paper, we propose a method to jointly optimize the transmit power, the number of bits per symbol and the CPU cycles assigned to each application in order to minimize the power consumption at the mobile side, under an average latency constraint dictated by the application requirements. We consider the case of a set of mobile handsets served by a single cloud and we show that the optimization leads to a one-to-one relationship between the transmit power and the percentage of CPU cycles assigned to each user. Based on our optimization, we propose then a computation scheduling technique and verify the stability of the computations queue. Then we show how these queues are affected by the degrees of freedom of the channels between mobile handsets and server.
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paper_title: Joint Energy Minimization and Resource Allocation in C-RAN with Mobile Cloud

paper_content:

Cloud radio access network (C-RAN) has emerged as a potential candidate of the next generation access network technology to address the increasing mobile traffic, while mobile cloud computing (MCC) offers a prospective solution to the resource-limited mobile user in executing computation intensive tasks. Taking full advantages of above two cloud-based techniques, C-RAN with MCC are presented in this paper to enhance both performance and energy efficiencies. In particular, this paper studies the joint energy minimization and resource allocation in C-RAN with MCC under the time constraints of the given tasks. We first review the energy and time model of the computation and communication. Then, we formulate the joint energy minimization into a non-convex optimization with the constraints of task executing time, transmitting power, computation capacity and fronthaul data rates. This non-convex optimization is then reformulated into an equivalent convex problem based on weighted minimum mean square error (WMMSE). The iterative algorithm is finally given to deal with the joint resource allocation in C-RAN with mobile cloud. Simulation results confirm that the proposed energy minimization and resource allocation solution can improve the system performance and save energy.
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paper_title: Game-theoretic Analysis of Computation Offloading for Cloudlet-based Mobile Cloud Computing

paper_content:

Mobile cloud computing (MC2) is emerging as a promising computing paradigm which helps alleviate the conflict between resource-constrained mobile devices and resource-consuming mobile applications through computation offloading. In this paper, we analyze the computation offloading problem in cloudlet-based mobile cloud computing. Different from most of the previous works which are either from the perspective of a single user or under the setting of a single wireless access point (AP), we research the computation offloading strategy of multiple users via multiple wireless APs. With the widespread deployment of WLAN, offloading via multiple wireless APs will obtain extensive application. Taking energy consumption and delay (including computing and transmission delay) into account, we present a game-theoretic analysis of the computation offloading problem while mimicking the selfish nature of the individuals. In the case of homogeneous mobile users, conditions of Nash equilibrium are analyzed, and an algorithm that admits a Nash equilibrium is proposed. For heterogeneous users, we prove the existence of Nash equilibrium by introducing the definition of exact potential game and design a distributed computation offloading algorithm to help mobile users choose proper offloading strategies. Numerical extensive simulations have been conducted and results demonstrate that the proposed algorithm can achieve desired system performance.
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paper_title: Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

paper_content:

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function , which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.
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paper_title: Optimal admission control policy for mobile cloud computing hotspot with cloudlet

paper_content:

We consider an admission control problem and adaptive resource allocation for running mobile applications on a cloudlet. We formulate an optimization problem for dynamic resource sharing of mobile users in mobile cloud computing (MCC) hotspot with a cloudlet as a semi-Markov decision process (SMDP). SMDP is transformed into a linear programming (LP) model and it is solved to obtain an optimal solution. In the optimization model, the quality of service (QoS) for different classes of mobile user is taken into account under resource constraints (i.e., bandwidth and server). The numerical results are presented to illustrate that the proposed admission control scheme can achieve a desirable performance and improve throughput of an MCC hotspot significantly.
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paper_title: Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing

paper_content:

In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving.
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paper_title: Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications

paper_content:

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, multi-user computation partitioning problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust , to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10 percent on average in terms of application delay. Based on SearchAdjust , we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces.
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paper_title: Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing

paper_content:

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.
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paper_title: Joint scheduling of communication and computation resources in multiuser wireless application offloading

paper_content:

We consider a system where multiple users are ::: connected to a small cell base station enhanced with ::: computational capabilities. Instead of doing the computation ::: locally at the handset, the users offload the computation of full ::: applications or pieces of code to the small cell base station. In this ::: scenario, this paper provides a strategy to allocate the uplink, ::: downlink, and remote computational resources. The goal is to ::: improve the quality of experience of the users, while achieving ::: energy savings with respect to the case in which the applications ::: run locally at the mobile terminals. More specifically, we focus on ::: minimizing a cost function that depends on the latencies ::: experienced by the users and provide an algorithm to minimize ::: the latency experienced by the worst case user, under a target ::: energy saving constraint per user.
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paper_title: Exploring device-to-device communication for mobile cloud computing

paper_content:

With the popularity of smartphones and explosion of mobile applications, mobile devices become the prevalent computing platform for convenient communication and rich entertainment. Mobile cloud computing (MCC) is proposed to overcome the limited resources of mobile systems. However, when users access MCC through wireless networks, cellular network is likely to be overloaded and Wi-Fi connectivity is intermittent. Therefore, device-to-device (D2D) communication is exploited as an alternative for MCC. An important issue in exploring D2D communication for MCC is how users can detect and utilize the computing resources on other mobile devices. In this paper, we propose two mobile cloud access schemes: optimal and periodic access schemes, and study the corresponding performance of mobile cloud computing (i.e., mobile cloud size, node's serviceable time percentage, and task success rate). We find that optimally, node's serviceable time percentage and task success rate approach 1. Using more practical periodic access scheme, node's serviceable time percentage and task success rate are determined by the ratio of contact and inter-contact time between two nodes.
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paper_title: Power-Delay Tradeoff in Multi-User Mobile-Edge Computing Systems

paper_content:

Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Specifically, at each time slot, the optimal frequencies of the local CPUs are obtained in closed forms, while the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method. Performance analysis is conducted for the proposed algorithm, which indicates that the power consumption and execution delay obeys an [O (1/V); O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters to the system performance.
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paper_title: Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading

paper_content:

Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the so-called femto-cloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints.
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paper_title: Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks

paper_content:

Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution.
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paper_title: Energy-traffic tradeoff cooperative offloading for mobile cloud computing

paper_content:

This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable trade- off between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN.
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paper_title: Decentralized and Optimal Resource Cooperation in Geo-Distributed Mobile Cloud Computing

paper_content:

Mobile cloud computing is a key enabling technology in the era of the Internet of Things. Geo-distributed mobile cloud computing (GMCC) is a new scenario that adds geography consideration in mobile cloud computing. In GMCC, users are able to access cloud resources that are geographically close to their mobile devices. This is expected to reduce the communication delay and the service providers’ cost compared with the traditional centralized approach. In this paper, we focus on resource sharing through the cooperation among the service providers in geo-distributed mobile cloud computing. Then, we propose two different strategies for efficient resource cooperation in geographically distributed data centers. Furthermore, we present a coalition game theoretical approach to deal with the competition and the cooperation among the service providers. Utility functions have been specifically considered to incorporate the cost related to virtual machine migration and resource utilization. Illustrative results indicate that our proposed schemes are able to efficiently utilize limited resources with quality-of-service consideration.
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paper_title: Joint offloading decision and resource allocation for mobile cloud with computing access point

paper_content:

We consider a mobile cloud computing system consisting of multiple users, one computing access point (CAP), and one remote cloud server. The CAP can either process the received tasks from mobile users or offload them to the cloud. We aim to jointly optimize the offloading decisions of all users and the CAP, together with communication and processing resource allocation, to minimize the overall cost of energy, computation, and the maximum delay among all users. It is shown that the problem can be formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. We further propose an efficient solution to this problem by semidefinite relaxation and a novel randomization mapping method. Our simulation results show that the proposed algorithm gives nearly optimal performance with only a small number of randomization iterations.
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paper_title: Challenges on wireless heterogeneous networks for mobile cloud computing

paper_content:

Mobile cloud computing (MCC) is an appealing paradigm enabling users to enjoy the vast computation power and abundant network services ubiquitously with the support of remote cloud. However, the wireless networks and mobile devices have to face many challenges due to the limited radio resources, battery power and communications capabilities, which may significantly impede the improvement of service qualities. Heterogeneous Network (HetNet), which has multiple types of low power radio access nodes in addition to the traditional macrocell nodes in a wireless network, is widely accepted as a promising way to satisfy the unrelenting traffic demand. In this article, we first introduce the framework of HetNet for MCC, identifying the main functional blocks. Then, the current state of the art techniques for each functional block are briefly surveyed, and the challenges for supporting MCC applications in HetNet under our proposed framework are discussed. We also envision the future for MCC in HetNet before drawing the conclusion.
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paper_title: A Framework for Cooperative Resource Management in Mobile Cloud Computing

paper_content:

Mobile cloud computing is an emerging technology to improve the quality of mobile services. In this paper, we consider the resource (i.e., radio and computing resources) sharing problem to support mobile applications in a mobile cloud computing environment. In such an environment, mobile cloud service providers can cooperate (i.e., form a coalition) to create a resource pool to share their own resources with each other. As a result, the resources can be better utilized and the revenue of the mobile cloud service providers can be increased. To maximize the benefit of the mobile cloud service providers, we propose a framework for resource allocation to the mobile applications, and revenue management and cooperation formation among service providers. For resource allocation to the mobile applications, we formulate and solve optimization models to obtain the optimal number of application instances that can be supported to maximize the revenue of the service providers while meeting the resource requirements of the mobile applications. For sharing the revenue generated from the resource pool (i.e., revenue management) among the cooperative mobile cloud service providers in a coalition, we apply the concepts of core and Shapley value from cooperative game theory as a solution. Based on the revenue shares, the mobile cloud service providers can decide whether to cooperate and share the resources in the resource pool or not. Also, the provider can optimize the decision on the amount of resources to contribute to the resource pool.
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paper_title: Mobility-Induced Service Migration in Mobile Micro-Clouds

paper_content:

Mobile micro-cloud is an emerging technology in distributed computing, which is aimed at providing seamless computing/data access to the edge of the network when a centralized service may suffer from poor connectivity and long latency. Different from the traditional cloud, a mobile micro-cloud is smaller and deployed closer to users, typically attached to a cellular base station or wireless network access point. Due to the relatively small coverage area of each base station or access point, when a user moves across areas covered by different base stations or access points which are attached to different micro-clouds, issues of service performance and service migration become important. In this paper, we consider such migration issues. We model the general problem as a Markov decision process (MDP), and show that, in the special case where the mobile user follows a one-dimensional asymmetric random walk mobility model, the optimal policy for service migration is a threshold policy. We obtain the analytical solution for the cost resulting from arbitrary thresholds, and then propose an algorithm for finding the optimal thresholds. The proposed algorithm is more efficient than standard mechanisms for solving MDPs.
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paper_title: A game theoretic resource allocation for overall energy minimization in mobile cloud computing system

paper_content:

Cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute code remotely. When the cloud infrastructure consists of heterogeneous servers, the mapping between mobile devices and servers plays an important role in determining the energy dissipation on both sides. From an environmental impact perspective, any energy dissipation related to computation should be counted. To achieve energy sustainability, it is important reducing the overall energy consumption of the mobile systems and the cloud infrastructure. Furthermore, reducing cloud energy consumption can potentially reduce the cost of mobile cloud users because the pricing model of cloud services is pay-by-usage. In this paper, we propose a game-theoretic approach to optimize the overall energy in a mobile cloud computing system. We formulate the energy minimization problem as a congestion game, where each mobile device is a player and his strategy is to select one of the servers to offload the computation while minimizing the overall energy consumption. We prove that the Nash equilibrium always exists in this game and propose an efficient algorithm that could achieve the Nash equilibrium in polynomial time. Experimental results show that our approach is able to reduce the total energy of mobile devices and servers compared to a random approach and an approach which only tries to reduce mobile devices alone.
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paper_title: A Cooperative Scheduling Scheme of Local Cloud and Internet Cloud for Delay-Aware Mobile Cloud Computing

paper_content:

With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can be deployed locally so that the latency is decreased. However, the computational resource of the local cloud is generally limited. In this paper, we design a threshold-based policy to improve the QoS of MCC by cooperation of the local cloud and Internet cloud resources, which takes the advantages of low latency of the local cloud and abundant computational resources of the Internet cloud simultaneously. This policy also applies a priority queue in terms of delay requirements of applications. The optimal thresholds depending on the traffic load is obtained via a proposed algorithm. Numerical results show that the QoS can be greatly enhanced with the assistance of Internet cloud when the local cloud is overloaded. Better QoS is achieved if the local cloud order tasks according to their delay requirements, where delay-sensitive applications are executed ahead of delay-tolerant applications. Moreover, the optimal thresholds of the policy have a sound impact on the QoS of the system.
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paper_title: Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

paper_content:

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
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paper_title: Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

paper_content:

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.
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paper_title: Modeling and Characterizing User Experience in a Cloud Server Based Mobile Gaming Approach

paper_content:

With the evolution of mobile devices and networks, and the growing trend of mobile Internet access, rich, multiplayer gaming using mobile devices, similar to PC-based Internet games, has tremendous potential and interest. However, the current client-server architecture for PC-based Internet games, where most of the storage and computational burden of the game lies with the client device, does not work with mobile devices, constraining mobile gaming to either downloadable, single player games, or very light non-interactive versions of the rich multi-player Internet games. In this paper, we study a cloud server based approach, termed Cloud Mobile Gaming (CMG), where the burden of executing the gaming engines is put on cloud servers, and the mobile devices just communicate the users' gaming commands to the servers. We analyze the factors affecting the quality of user experience using the CMG approach, including the game genres, video encoding factors, and the conditions of the wireless network. Based on the above analysis, we develop a model for Mobile Gaming User Experience (MGUE), and develop a prototype for real-time measurement of MGUE that can be used in real networks. We validate MGUE model using controlled subjective testing, and then use it to characterize user experience achievable using the CMG approach in wireless networks.
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paper_title: Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks

paper_content:

Current estimates of mobile data traffic in the years to come foresee a 1,000 increase of mobile data traffic in 2020 with respect to 2010, or, equivalently, a doubling of mobile data traffic every year. This unprecedented growth demands a significant increase of wireless network capacity. Even if the current evolution of fourth-generation (4G) systems and, in particular, the advancements of the long-term evolution (LTE) standardization process foresees a significant capacity improvement with respect to third-generation (3G) systems, the European Telecommunications Standards Institute (ETSI) has established a roadmap toward the fifth-generation (5G) system, with the aim of deploying a commercial system by the year 2020 [1]. The European Project named ?Mobile and Wireless Communications Enablers for the 2020 Information Society? (METIS), launched in 2012, represents one of the first international and large-scale research projects on fifth generation (5G) [2]. In parallel with this unparalleled growth of data traffic, our everyday life experience shows an increasing habit to run a plethora of applications specifically devised for mobile devices, (smartphones, tablets, laptops)for entertainment, health care, business, social networking, traveling, news, etc. However, the spectacular growth in wireless traffic generated by this lifestyle is not matched with a parallel improvement on mobile handsets? batteries, whose lifetime is not improving at the same pace [3]. This determines a widening gap between the energy required to run sophisticated applications and the energy available on the mobile handset. A possible way to overcome this obstacle is to enable the mobile devices, whenever possible and convenient, to offload their most energy-consuming tasks to nearby fixed servers. This strategy has been studied for a long time and is reported in the literature under different names, such as cyberforaging [4] or computation offloading [5], [6]. In recent years, a strong impulse to computation offloading has come through cloud computing (CC), which enables the users to utilize resources on demand. The resources made available by a cloud service provider are: 1) infrastructures, such as network devices, storage, servers, etc., 2) platforms, such as operating systems, offering an integrated environment for developing and testing custom applications, and 3) software, in the form of application programs. These three kinds of services are labeled, respectively, as infrastructure as a service, platform as a service, and software as a service. In particular, one of the key features of CC is virtualization, which makes it possible to run multiple operating systems and multiple applications over the same machine (or set of machines), while guaranteeing isolation and protection of the programs and their data. Through virtualization, the number of virtual machines (VMs) can scale on ?demand, thus improving the overall system computational efficiency. Mobile CC (MCC) is a specific case of CC where the user accesses the cloud services through a mobile handset [5]. The major limitations of today?s MCC are the energy consumption associated to the radio access and the latency experienced in reaching the cloud provider through a wide area network (WAN). Mobile users located at the edge of macrocellular networks are particularly disadvantaged in terms of power consumption and, furthermore, it is very difficult to control latency over a WAN. As pointed out in [7]?[9], humans are acutely sensitive to delay and jitter: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems, in particular in the so-called tactile Internet [10], are quite small (in the order of milliseconds), a strict latency control must be somehow incorporated in near future MCC. Meeting this constraint requires a deep ?rethinking of the overall service chain, from the physical layer up to virtualization.
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paper_title: Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing

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In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving.
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paper_title: Fog and IoT: An Overview of Research Opportunities

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Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.
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paper_title: Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

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This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.
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paper_title: Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading

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Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the so-called femto-cloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints.
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paper_title: Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

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Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles/second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.
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paper_title: Large-Scale Convex Optimization for Dense Wireless Cooperative Networks

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Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.
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paper_title: Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

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This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
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paper_title: Stochastic geometry and random graphs for the analysis and design of wireless networks

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Wireless networks are fundamentally limited by the intensity of the received signals and by their interference. Since both of these quantities depend on the spatial location of the nodes, mathematical techniques have been developed in the last decade to provide communication-theoretic results accounting for the networks geometrical configuration. Often, the location of the nodes in the network can be modeled as random, following for example a Poisson point process. In this case, different techniques based on stochastic geometry and the theory of random geometric graphs -including point process theory, percolation theory, and probabilistic combinatorics-have led to results on the connectivity, the capacity, the outage probability, and other fundamental limits of wireless networks. This tutorial article surveys some of these techniques, discusses their application to model wireless networks, and presents some of the main results that have appeared in the literature. It also serves as an introduction to the field for the other papers in this special issue.
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paper_title: Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

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With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
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paper_title: An Approach to Ad hoc Cloud Computing

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We consider how underused computing resources within an enterprise may be harnessed to improve utilization and create an elastic computing infrastructure. Most current cloud provision involves a data center model, in which clusters of machines are dedicated to running cloud infrastructure software. We propose an additional model, the ad hoc cloud, in which infrastructure software is distributed over resources harvested from machines already in existence within an enterprise. In contrast to the data center cloud model, resource levels are not established a priori, nor are resources dedicated exclusively to the cloud while in use. A participating machine is not dedicated to the cloud, but has some other primary purpose such as running interactive processes for a particular user. We outline the major implementation challenges and one approach to tackling them.
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paper_title: An enhanced community-based mobility model for distributed mobile social networks

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Simulation is fundamental tool for the evaluation and validation of the applications and protocols in Mobile Social Networks. However, the limited number of real user traces available and the imposed restrictions of the specific scenarios, make generalization very hard. Therefore, the need has been created for synthetic mobility models. The widely used Random Way-Point Mobility Model has been proven unable to capture characteristics of human mobility such as the social attraction. Consequently, in recent years mobility models based on social network theory, able to capture the temporal and spatial dependencies of mobile social networks, are being designed. In this paper the Enhanced Community Mobility Model (ECMM) is introduced. It follows preceding community-based approaches, that map communities to a topological space. Its main contribution is the introduction of new features, such as pause periods and group mobility encouragement, lacking for previous community-based mobility models. Additionally, ECMM enables researchers to arbitrarily select a social model as the trace generation process input, while at the same time generates traces with high conformance to that social network. A comparison between synthetic traces, generated by ECMM, other community-based models and a number of real ones is provided for validation.
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paper_title: Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems

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Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.
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paper_title: A hierarchical edge cloud architecture for mobile computing

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The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations.
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paper_title: Fog Computing: Focusing on Mobile Users at the Edge

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With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective.
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paper_title: Mobile-Edge Computing Come Home Connecting things in future smart homes using LTE device-to-device communications

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Future 5G cellular networks are expected to play a major role in supporting the Internet of Things (IoT) due to their ubiquitous coverage, plug-and-play configuration, and embedded security. Besides connectivity, however, the IoT will need computation and storage in proximity of sensors and actuators to support timecritical and opportunistic applications. Mobile-edge computing (MEC) is currently under standardization as a novel paradigm expected to enrich future broadband communication networks [1], [2]. With MEC, traditional networks will be empowered by placing cloud-computing-like capabilities within the radio access network, in an MEC server located in close proximity to end users. Such distributed computing and storage infrastructure will enable the deployment of applications and services at the edge of the network, allowing operators to offer a virtualized environment to enterprise customers and industries to implement applications and services close to end users.
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paper_title: Challenges on wireless heterogeneous networks for mobile cloud computing

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Mobile cloud computing (MCC) is an appealing paradigm enabling users to enjoy the vast computation power and abundant network services ubiquitously with the support of remote cloud. However, the wireless networks and mobile devices have to face many challenges due to the limited radio resources, battery power and communications capabilities, which may significantly impede the improvement of service qualities. Heterogeneous Network (HetNet), which has multiple types of low power radio access nodes in addition to the traditional macrocell nodes in a wireless network, is widely accepted as a promising way to satisfy the unrelenting traffic demand. In this article, we first introduce the framework of HetNet for MCC, identifying the main functional blocks. Then, the current state of the art techniques for each functional block are briefly surveyed, and the challenges for supporting MCC applications in HetNet under our proposed framework are discussed. We also envision the future for MCC in HetNet before drawing the conclusion.
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paper_title: Femtocells: Past, Present, and Future

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Femtocells, despite their name, pose a potentially large disruption to the carefully planned cellular networks that now connect a majority of the planet's citizens to the Internet and with each other. Femtocells - which by the end of 2010 already outnumbered traditional base stations and at the time of publication are being deployed at a rate of about five million a year - both enhance and interfere with this network in ways that are not yet well understood. Will femtocells be crucial for offloading data and video from the creaking traditional network? Or will femtocells prove more trouble than they are worth, undermining decades of careful base station deployment with unpredictable interference while delivering only limited gains? Or possibly neither: are femtocells just a "flash in the pan"; an exciting but short-lived stage of network evolution that will be rendered obsolete by improved WiFi offloading, new backhaul regulations and/or pricing, or other unforeseen technological developments? This tutorial article overviews the history of femtocells, demystifies their key aspects, and provides a preview of the next few years, which the authors believe will see a rapid acceleration towards small cell technology. In the course of the article, we also position and introduce the articles that headline this special issue.
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paper_title: Success Probability and Area Spectral Efficiency in Multiuser MIMO HetNets

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We derive a general and closed-form result for the success probability in downlink multiple-antenna (MIMO) heterogeneous cellular networks (HetNets), utilizing a novel Toeplitz matrix representation. This main result, which is equivalently the signal-to-interference ratio (SIR) distribution, includes multiuser MIMO, single-user MIMO and per-tier biasing for $K$ different tiers of randomly placed base stations (BSs), assuming zero-forcing precoding and perfect channel state information. The large SIR limit of this result admits a simple closed form that is accurate at moderate SIRs, e.g., above 5 dB. These results reveal that the SIR-invariance property of SISO HetNets does not hold for MIMO HetNets; instead the success probability may decrease as the network density increases. We prove that the maximum success probability is achieved by activating only one tier of BSs, while the maximum area spectral efficiency (ASE) is achieved by activating all the BSs. This reveals a unique tradeoff between the ASE and link reliability in multiuser MIMO HetNets. To achieve the maximum ASE while guaranteeing a certain link reliability, we develop efficient algorithms to find the optimal BS densities. It is shown that as the link reliability requirement increases, more BSs and more tiers should be deactivated.
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paper_title: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

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In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.
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paper_title: Modeling and Characterizing User Experience in a Cloud Server Based Mobile Gaming Approach

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With the evolution of mobile devices and networks, and the growing trend of mobile Internet access, rich, multiplayer gaming using mobile devices, similar to PC-based Internet games, has tremendous potential and interest. However, the current client-server architecture for PC-based Internet games, where most of the storage and computational burden of the game lies with the client device, does not work with mobile devices, constraining mobile gaming to either downloadable, single player games, or very light non-interactive versions of the rich multi-player Internet games. In this paper, we study a cloud server based approach, termed Cloud Mobile Gaming (CMG), where the burden of executing the gaming engines is put on cloud servers, and the mobile devices just communicate the users' gaming commands to the servers. We analyze the factors affecting the quality of user experience using the CMG approach, including the game genres, video encoding factors, and the conditions of the wireless network. Based on the above analysis, we develop a model for Mobile Gaming User Experience (MGUE), and develop a prototype for real-time measurement of MGUE that can be used in real networks. We validate MGUE model using controlled subjective testing, and then use it to characterize user experience achievable using the CMG approach in wireless networks.
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paper_title: EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments

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With the increasing prevalence of large scale cloud computing environments, how to place requested applications into available computing servers regarding to energy consumption has become an essential research problem, but existing application placement approaches are still not effective for live applications with dynamic characters. In this paper, we proposed a novel approach named EnaCloud, which enables application live placement dynamically with consideration of energy efficiency in a cloud platform. In EnaCloud, we use a Virtual Machine to encapsulate the application, which supports applications scheduling and live migration to minimize the number of running machines, so as to save energy. Specially, the application placement is abstracted as a bin packing problem, and an energy-aware heuristic algorithm is proposed to get an appropriate solution. In addition, an over-provision approach is presented to deal with the varying resource demands of applications. Our approach has been successfully implemented as useful components and fundamental services in the iVIC platform. Finally, we evaluate our approach by comprehensive experiments based on virtual machine monitor Xen and the results show that it is feasible.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

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Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

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Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network connection bandwidth. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NP-completeness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.
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paper_title: Cache-enabled small cell networks: modeling and tradeoffs

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We consider the problem of caching in next generation mobile cellular networks where small base stations (SBSs) are able to store their users' content and serve them accordingly. The SBSs are stochastically distributed over the plane and serve their users either from the local cache or internet via limited backhaul, depending on the availability of requested content. We model and characterize the outage probability and average content delivery rate as a function of the signal-to-interference-ratio (SINR), base station intensity, target file bitrate, storage size and file popularity. Our results provide key insights into the problem of cache-enabled small cell networks.
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paper_title: Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Information-Centric Networks

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Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we jointly consider caching and multicasting to maximize the successful transmission probability in large-scale information-centric networks. We propose a random caching and multicasting scheme with a design parameter. Utilizing tools from stochastic geometry, we derive a tractable expression and a closed-form expression for the successful transmission probability in the general and high signal-to-noise ratio (SNR) regions, respectively. Then, using optimization techniques, we derive a simple asymptotically optimal design in the high SNR region, which provides important design insights. Finally, by numerical simulations, we show that the asymptotically optimal design also achieves a significant performance gain over some baseline schemes in the general SNR region, and hence is applicable and effective in practical cache-enabled information-centric networks.
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paper_title: Scheduling strategies for optimal service deployment across multiple clouds

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The current cloud market, constituted by many different public cloud providers, is highly fragmented in terms of interfaces, pricing schemes, virtual machine offers and value-added features. In this context, a cloud broker can provide intermediation and aggregation capabilities to enable users to deploy their virtual infrastructures across multiple clouds. However, most current cloud brokers do not provide advanced service management capabilities to make automatic decisions, based on optimization algorithms, about how to select the optimal cloud to deploy a service, how to distribute optimally the different components of a service among different clouds, or even when to move a given service component from a cloud to another to satisfy some optimization criteria. In this paper we present a modular broker architecture that can work with different scheduling strategies for optimal deployment of virtual services across multiple clouds, based on different optimization criteria (e.g. cost optimization or performance optimization), different user constraints (e.g. budget, performance, instance types, placement, reallocation or load balancing constraints), and different environmental conditions (i.e., static vs. dynamic conditions, regarding instance prices, instance types, service workload, etc.). To probe the benefits of this broker, we analyse the deployment of different clustered services (an HPC cluster and a Web server cluster) on a multi-cloud environment under different conditions, constraints, and optimization criteria.
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paper_title: Cache in the air: exploiting content caching and delivery techniques for 5G systems

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The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.
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paper_title: Cost Aware Service Placement and Load Dispatching in Mobile Cloud Systems

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With proliferation of smart phones and an increasing number of services provisioned by clouds, it is commonplace for users to request cloud services from their mobile devices. Accessing services directly from the Internet data centers inherently incurs high latency due to long RTTs and possible congestions in WAN. To lower the latency, some researchers propose to ‘cache’ the services at edge clouds or smart routers in the access network which are closer to end users than the Internet cloud. Although ‘caching’ is a promising technique, placing the services and dispatching users’ requests in a way that can minimize the users’ access delay and service providers’ cost has not been addressed so far. In this paper, we study the joint optimization of service placement and load dispatching in the mobile cloud systems. We show this problem is unique to both the traditional caching problem in mobile networks and the content distribution problem in content distribution networks. We develop a set of efficient algorithms for service providers to achieve various trade-offs among the average latency of mobile users’ requests, and the cost of service providers. Our solution utilizes user's mobility pattern and services access pattern to predict the distribution of user's future requests, and then adapt the service placement and load dispatching online based on the prediction. We conduct extensive trace driven simulations. Results show our solution not only achieves much lower latency than directly accessing service from remote clouds, but also outperforms other classical benchmark algorithms in term of the latency, cost and algorithm running time.
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paper_title: FemtoCaching: Wireless video content delivery through distributed caching helpers

paper_content:

We suggest a novel approach to handle the ongoing explosive increase in the demand for video content in wireless/mobile devices. We envision femtocell-like base stations, which we call helpers, with weak backhaul links but large storage capacity. These helpers form a wireless distributed caching network that assists the macro base station by handling requests of popular files that have been cached. Due to the short distances between helpers and requesting devices, the transmission of cached files can be done very efficiently.
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paper_title: User Mobility Model Based Computation Offloading Decision for Mobile Cloud

paper_content:

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users’ mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.
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paper_title: Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

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Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and image storage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities. Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of our knowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobile cloud, an approach we call k-out-of-n computing . In our solution, mobile devices successfully retrieve or process data, in the most energy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibility of our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in larger scale networks.
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paper_title: Mobility-Assisted Opportunistic Computation Offloading

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With the explosion in personal mobile devices, computation offloading through opportunistic networks of nearby devices to support sophisticated mobile applications with limited resources of processing and energy is gaining increased attention. In this letter, we investigate the problem of mobility-assisted opportunistic computation offloading by exploiting the contact patterns regulated by these devices' mobility. We first formulate the optimal opportunistic offloading problem with the aid of statistic property of contact rates and then utilize the method of convex optimization to determine the amounts of computation to be offloaded to other devices. Extensive simulations under realistic human mobility demonstrate the efficiency of our scheme, in terms of obtaining superior higher computation success rate over the compared benchmarks under a wide variety of settings.
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paper_title: An overview of vertical handover decision strategies in heterogeneous wireless networks

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In the next generation of wireless networks, mobile users can move between heterogeneous networks, using terminals with multiple access interfaces and non-real-time or real-time services. The most important issue in such environment is the Always Best Connected (ABC) concept allowing the best connectivity to applications anywhere at anytime. To answer ABC requirement, various vertical handover decision strategies have been proposed in the literature recently, using advanced tools and proven concepts. In this paper, we give an overview of the most interesting and recent strategies. We classify it into five categories for which we present their main characteristics. We also compare each one with the others in order to introduce our vertical handover decision approach.
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paper_title: Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

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The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users’ local load and availability of cloudlets. We examine users’ mobility patterns and cloudlets’ admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.
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paper_title: Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

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Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function , which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.
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paper_title: MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing

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This paper exploits the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. User Mobility introduces new complexities in enabling an optimal decomposition of tasks that can execute cooperatively on mobile clients and the tiered cloud architecture while considering multiple QoS goals such application delay, device power consumption and user cost/price. In this paper, we propose a novel framework to model mobile applications as a location-time workflows (LTW) of tasks, here user mobility patterns are translated to a mobile service usage patterns. We show that an optimal mapping of LTWs to tiered mobile cloud resources is an NP-hard problem. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (78% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing and video streaming applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint, Manhattan models and is resilient to errors/uncertainty in prediction of mobile user location-time workflows.
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paper_title: Mobility management challenges in 3GPP heterogeneous networks

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In this article we provide a comprehensive review of the handover process in heterogeneous networks (HetNets), and identify technical challenges in mobility management. In this line, we evaluate the mobility performance of HetNets with the 3rd Generation Partnership Project (3GPP) Release-10 range expansion and enhanced inter-cell interference coordination (eICIC) features such as almost blank subframes (ABSFs). Simulation assumptions and parameters of a related study item in 3GPP are used to investigate the impact of various handover parameters on mobility performance. In addition, we propose a mobility-based inter-cell interference coordination (MB-ICIC) scheme, in which picocells configure coordinated resources so that macrocells can schedule their high-mobility UEs in these resources without co-channel interference from picocells. MB-ICIC also benefits low-mobility UEs, since handover parameters can now be more flexibly optimized. Simulations using the 3GPP simulation assumptions are performed to evaluate the performance of MB-ICIC under several scenarios.
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paper_title: Efficient mobility and traffic management for delay tolerant cloud data in 5G networks

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The explosive growth of the demand for higher data rates in mobile networks have been mainly driven by the increasing use of cloud based applications by smartphones. This has led the industry to investigate new radio access technologies to be deployed as part of 5G networks, while providing mechanisms to manage user mobility and traffic in a more efficient manner. In this paper, we consider a mobility and traffic management mechanism that proposes a close interaction between the cloud data servers and the radio access network to enable efficient network operation. Such a management mechanism is enabled by utilizing the application-dependent delay tolerance properties of the cloud data, with the delay values conveyed to the radio access network and UE to manage the service requests for the cloud data. The mechanism was evaluated using LTE-Advanced heterogeneous network scenario and 5G dense-urban information society scenario from EU FP7 METIS project, and relative gains in terms of packet delays and throughput values are presented. The results indicate significant gains using the proposed management mechanism as compared to the reference case where no such enhancements are used.
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paper_title: Exploring device-to-device communication for mobile cloud computing

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With the popularity of smartphones and explosion of mobile applications, mobile devices become the prevalent computing platform for convenient communication and rich entertainment. Mobile cloud computing (MCC) is proposed to overcome the limited resources of mobile systems. However, when users access MCC through wireless networks, cellular network is likely to be overloaded and Wi-Fi connectivity is intermittent. Therefore, device-to-device (D2D) communication is exploited as an alternative for MCC. An important issue in exploring D2D communication for MCC is how users can detect and utilize the computing resources on other mobile devices. In this paper, we propose two mobile cloud access schemes: optimal and periodic access schemes, and study the corresponding performance of mobile cloud computing (i.e., mobile cloud size, node's serviceable time percentage, and task success rate). We find that optimally, node's serviceable time percentage and task success rate approach 1. Using more practical periodic access scheme, node's serviceable time percentage and task success rate are determined by the ratio of contact and inter-contact time between two nodes.
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paper_title: Reservation-based resource scheduling and code partition in mobile cloud computing

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Mobile cloudlet has shown great potential to extend the computational limitation of mobile devices. The overall performance of the mobile applications could be improved when the computational gain from the cloudlet overweights the transmission cost. Yet, resources scheduling on the cloudlet is a challenging problem when there are multiple mobile offloading requests at the same time. Since the offloading requests would compete with each other and each offloading decision would impact the others, finding the optimal execution layout is quite difficult. In this paper, we propose a joint resource scheduling and code partition scheme that can efficiently allocate cloudlet resources to multiple mobile users. Our goal is to maximize the cloudlet throughput as well as to reduce the mobile application's execution time. Unlike the current approach which considers an infinite resource limitation on the cloud, our algorithm dynamically allocate the cloudlet resource based on the current usage. Both real world experiments and trace-driven simulations show that our solution can improve the cloudlet throughput by 20%-35% in the meantime provide speedup to mobile devices.
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paper_title: Optimization of Resource Provisioning Cost in Cloud Computing

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In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.
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paper_title: Mobility-Aware Caching for Content-Centric Wireless Networks: Modeling and Methodology

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As mobile services are shifting from connection- centric communications to content-centric communications, content-centric wireless networking emerges as a promising paradigm to evolve the current network architecture. Caching popular content at the wireless edge, including base stations and user terminals, provides an effective approach to alleviate the heavy burden on backhaul links, as well as lower delays and deployment costs. In contrast to wired networks, a unique characteristic of content-centric wireless networks (CCWNs) is the mobility of mobile users. While it has rarely been considered by existing works on caching design, user mobility contains various helpful side information that can be exploited to improve caching efficiency at both BSs and user terminals. In this article, we present a general framework for mobility-aware caching in CCWNs. Key properties of user mobility patterns that are useful for content caching are first identified, and then different design methodologies for mobility-aware caching are proposed. Moreover, two design examples are provided to illustrate the proposed framework in detail, and interesting future research directions are identified.
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paper_title: Temperature Aware Workload Management in Geo-distributed Datacenters.

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Lately, for geo-distributed data centers, a workload management approach that routes user requests to locations with cheaper and cleaner electricity has been developed to reduce energy consumption and cost. We consider two key aspects that have not been explored in this approach. First, through empirical studies, we find that the energy efficiency of cooling systems depends critically on the ambient temperature, which exhibits significant geographical diversity. Temperature diversity can be used to reduce the cooling energy overhead. Second, energy consumption comes from not only interactive workloads driven by user requests, but also delay tolerant batch workloads that run at the back-end. The elastic nature of batch workloads can be exploited to further reduce the energy cost. In this paper, we propose to make workload management temperature aware . We formulate the problem as a joint optimization of request routing for interactive workloads and capacity allocation for batch workloads. We develop a distributed algorithm based on an $m$ -block alternating direction method of multipliers (ADMM) algorithm that extends the classical two-block algorithm. We prove the convergence and rate of convergence results under general assumptions. Through trace-driven simulations, we find that our approach consistently provides 15-20 percent cooling energy reduction, and 5-20 percent overall cost reduction over existing methods.
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paper_title: Energy Efficient Resource Management in Virtualized Cloud Data Centers

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Rapid growth of the demand for computational power by scientific, business and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. We propose an energy efficient resource management system for virtualized Cloud data centers that reduces operational costs and provides required Quality of Service (QoS). Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs and thermal state of computing nodes. We present first results of simulation-driven evaluation of heuristics for dynamic reallocation of VMs using live migration according to current requirements for CPU performance. The results show that the proposed technique brings substantial energy savings, while ensuring reliable QoS. This justifies further investigation and development of the proposed resource management system.
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paper_title: Optimal Power Allocation for Energy Harvesting and Power Grid Coexisting Wireless Communication Systems

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This paper considers the power allocation of a single-link wireless communication with joint energy harvesting and grid power supply. We formulate the problem as minimizing the grid power consumption with random energy and data arrival in fading channel, and analyze the structure of the optimal power allocation policy in some special cases. For the case that all the packets are arrived before transmission, it is a dual problem of throughput maximization, and the optimal solution is found by the two-stage water filling (WF) policy, which allocates the harvested energy in the first stage, and then allocates the power grid energy in the second stage. For the random data arrival case, we first assume grid energy or harvested energy supply only, and then combine the results to obtain the optimal structure of the coexisting system. Specifically, the reverse multi-stage WF policy is proposed to achieve the optimal power allocation when the battery capacity is infinite. Finally, some heuristic online schemes are proposed, of which the performance is evaluated by numerical simulations.
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paper_title: Green Cloudlet Network: A Distributed Green Mobile Cloud Network

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This article introduces a green cloudlet network (GCN) architecture in the context of mobile cloud computing. The proposed architecture is aimed at providing seamless and low end-to-end delay between a UE and its Avatar (its software clone) in the cloudlets to facilitate the application workloads offloading process. Furthermore, an SDN-based core network is introduced in the GCN architecture by replacing the traditional Evolved Packet Core in the LTE network in order to provide efficient communications connections between different end points. The Cloudlet Network File System (CNFS) is designed based on the proposed architecture in order to protect the Avatars’ dataset against hardware failures and improve Avatars’ performance in terms of data access latency. Moreover, a green energy supplement is proposed in the architecture in order to reduce the extra OPEX and CO2 footprint incurred by running the distributed cloudlets. Due to the temporal and spatial dynamics of both the green energy generation and energy demands of green cloudlet systems (GCSs), designing an optimal green energy management strategy based on the characteristics of green energy generation and the energy demands of eNBs and cloudlets to minimize the on-grid energy consumption is critical to the cloudlet provider.
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paper_title: The Case for Energy-Proportional Computing

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Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems.
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paper_title: Online Learning for Offloading and Autoscaling in Renewable-Powered Mobile Edge Computing

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Mobile edge computing (a.k.a. fog computing) has recently emerged to enable \emph{in-situ} processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning.
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paper_title: Dynamic virtual machine management via approximate Markov decision process

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Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs' resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter category are heuristical and lack theoretical performance guarantees. In this work, we formulate dynamic VM management as a large-scale Markov Decision Process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, MadVM can be implemented in a distributed system, which should suit the needs of real data centers. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.
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paper_title: Energy Harvesting Small Cell Networks: Feasibility, Deployment and Operation

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Small cell networks have attracted a great deal of attention in recent years due to their potential to meet the exponential growth of mobile data traffic, and the increasing demand for better quality of service and user experience in mobile applications. Nevertheless, wide deployment of small cell networks has not happened yet because of the complexity in the network planning and optimization, as well as the high expenditure involved in deployment and operation. In particular, it is difficult to provide grid power supply to all the small cell base stations in a cost-effective way. Moreover, a dense deployment of small cell base stations, which is needed to meet the capacity and coverage of next generation wireless networks, will increase operators' electricity bills and lead to significant carbon emission. Thus, it is crucial to exploit off-grid and green energy sources to power small cell networks, for which energy harvesting technology is a viable solution. In this article, we conduct a comprehensive study of energy harvesting small cell networks, and investigate important aspects, including a feasibility analysis, network deployment, and network operation issues. The advantages as well as unique challenges of energy harvesting small cell networks are highlighted, together with potential solutions and effective design methodologies.
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paper_title: Enabling Wireless Power Transfer in Cellular Networks: Architecture, Modeling and Deployment

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Microwave power transfer (MPT) delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. This provides mobiles practically infinite battery lives and eliminates the need of power cords and chargers. To enable MPT for mobile charging, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. The deployment of the hybrid network under an outage constraint on data links is investigated based on a stochastic-geometry model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes (PPPs) and single-antenna mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, mobiles and PBs fix their transmission power at p and q, respectively; a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is applied to derive the tradeoffs between the network parameters including p, q, and the BS/PB densities under the outage constraint. First, consider the deployment of the cellular network. It is proved that the outage constraint is satisfied so long as the product the BS density decreases with increasing p following a power law where the exponent is proportional to the path-loss exponent. Next, consider the deployment of the hybrid network assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product between q, the PB density, and the BS density raised to a power proportional to the path-loss exponent has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, a similar result is obtained with the aforementioned product increased by the array gain. Last, similar results are derived for the case of mobiles having small energy storage.
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paper_title: Energy Harvesting Sensor Nodes: Survey and Implications

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Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.
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paper_title: Energy Efficient Resource Allocation for Wireless Power Transfer Enabled Collaborative Mobile Clouds

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In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes.
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paper_title: Grid Energy Consumption and QoS Tradeoff in Hybrid Energy Supply Wireless Networks

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Hybrid energy supply (HES) wireless networks have recently emerged as a new paradigm to enable green networks, which are powered by both the electric grid and harvested renewable energy. In this paper, we will investigate two critical but conflicting design objectives of HES networks, i.e., the grid energy consumption and quality of service (QoS). Minimizing grid energy consumption by utilizing the harvested energy will make the network environmentally friendly, but the achievable QoS may be degraded due to the intermittent nature of energy harvesting. To investigate the tradeoff between these two aspects, we introduce the total service cost as the performance metric, which is the weighted sum of the grid energy cost and the QoS degradation cost. Base station assignment and power control is adopted as the main strategy to minimize the total service cost, while both cases with non-causal and causal side information are considered. With non-causal side information, a Greedy Assignment algorithm with low complexity and near-optimal performance is proposed. With causal side information, the design problem is formulated as a discrete Markov decision problem. Interesting solution structures are derived, which shall help to develop an efficient monotone backward induction algorithm. To further reduce complexity, a Look-Ahead policy and a Threshold-based Heuristic policy are also proposed. Simulation results shall validate the effectiveness of the proposed algorithms and demonstrate the unique grid energy consumption and QoS tradeoff in HES networks.
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paper_title: Throughput Maximization in Wireless Powered Communication Networks

paper_content:

This paper studies the newly emerging wireless powered communication network in which one hybrid access point (H-AP) with constant power supply coordinates the wireless energy/information transmissions to/from a set of distributed users that do not have other energy sources. A "harvest-then-transmit" protocol is proposed where all users first harvest the wireless energy broadcast by the H-AP in the downlink (DL) and then send their independent information to the H-AP in the uplink (UL) by time-division-multiple-access (TDMA). First, we study the sum-throughput maximization of all users by jointly optimizing the time allocation for the DL wireless power transfer versus the users' UL information transmissions given a total time constraint based on the users' DL and UL channels as well as their average harvested energy values. By applying convex optimization techniques, we obtain the closed-form expressions for the optimal time allocations to maximize the sum-throughput. Our solution reveals an interesting "doubly near-far" phenomenon due to both the DL and UL distance-dependent signal attenuation, where a far user from the H-AP, which receives less wireless energy than a nearer user in the DL, has to transmit with more power in the UL for reliable information transmission. As a result, the maximum sum-throughput is shown to be achieved by allocating substantially more time to the near users than the far users, thus resulting in unfair rate allocation among different users. To overcome this problem, we furthermore propose a new performance metric so-called common-throughput with the additional constraint that all users should be allocated with an equal rate regardless of their distances to the H-AP. We present an efficient algorithm to solve the common-throughput maximization problem. Simulation results demonstrate the effectiveness of the common-throughput approach for solving the new doubly near-far problem in wireless powered communication networks.
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paper_title: Green Data Centers: A Survey, Perspectives, and Future Directions

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At present, a major concern regarding data centers is their extremely high energy consumption and carbon dioxide emissions. However, because of the over-provisioning of resources, the utilization of existing data centers is, in fact, remarkably low, leading to considerable energy waste. Therefore, over the past few years, many research efforts have been devoted to increasing efficiency for the construction of green data centers. The goal of these efforts is to efficiently utilize available resources and to reduce energy consumption and thermal cooling costs. In this paper, we provide a survey of the state-of-the-art research on green data center techniques, including energy efficiency, resource management, thermal control and green metrics. Additionally, we present a detailed comparison of the reviewed proposals. We further discuss the key challenges for future research and highlight some future research issues for addressing the problem of building green data centers.
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paper_title: Robust Workload and Energy Management for Sustainable Data Centers

paper_content:

A large number of geo-distributed data centers begin to surge in the era of data deluge and information explosion. To meet the growing demand in massive data processing, the infrastructure of future data centers must be energy-efficient and sustainable. Facing this challenge, a systematic framework is put forth in this paper to integrate renewable energy sources (RES), distributed storage units, cooling facilities, as well as dynamic pricing into the workload and energy management tasks of a data center network. To cope with RES uncertainty, the resource allocation task is formulated as a robust optimization problem minimizing the worst-case net cost. Compared with existing stochastic optimization methods, the proposed approach entails a deterministic uncertainty set where generated RES reside, thus can be readily obtained in practice. It is further shown that the problem can be cast as a convex program, and then solved in a distributed fashion using the dual decomposition method. By exploiting the spatio-temporal diversity of local temperature, workload demand, energy prices, and renewable availability, the proposed approach outperforms existing alternatives, as corroborated by extensive numerical tests performed using real data.
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paper_title: Green-aware workload scheduling in geographically distributed data centers

paper_content:

Renewable (or green) energy, such as solar or wind, has at least partially powered data centers to reduce the environmental impact of traditional energy sources (brown energy with high carbon footprint). In this paper, we propose a holistic workload scheduling algorithm to minimize the brown energy consumption across multiple geographically distributed data centers with renewable energy sources. While green energy supply for a single data center is intermittent due to daily/seasonal effects, our workload scheduling algorithm is aware of different amounts of green energy supply and dynamically schedules the workload across data centers. The scheduling decision adapts to workload and data center cooling dynamics. Our experiments with real workload traces demonstrate that our scheduling algorithm greatly reduces brown energy consumption by up to 40% in comparison with other scheduling policies.
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paper_title: Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

paper_content:

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.
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paper_title: Energy Harvesting Wireless Communications: A Review of Recent Advances

paper_content:

This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.
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paper_title: Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices

paper_content:

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
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paper_title: Transmit Power Minimization for Wireless Networks with Energy Harvesting Relays

paper_content:

Energy harvesting (EH) has recently emerged as a key technology for green communications as it can power wireless networks with renewable energy sources. However, directly replacing the conventional non-EH transmitters by EH nodes will be a challenge. In this paper, we propose to deploy extra EH nodes as relays over an existing non-EH network. Specifically, the considered non-EH network consists of multiple source-destination (S-D) pairs. The deployed EH relays will take turns to assist each S-D pair, and energy diversity can be achieved to combat the low-EH rate of each EH relay. To make the best of these EH relays, with the source transmit power minimization as the design objective, we formulate a joint power assignment and relay selection problem, which, however, is NP-hard. We thus propose a general framework to develop efficient suboptimal algorithms, which is mainly based on a sufficient condition for the feasibility of the optimization problem. This condition yields useful design insights and also reveals an energy hardening effect, which provides the possibility to exempt the requirement of noncausal EH information. Simulation results will show that the proposed co-operation strategy can achieve near-optimal performance and provide significant power savings. Compared to the greedy co-operation method that only optimizes the performance of the current transmission block, the proposed strategy can achieve the same performance with much fewer relays, and the performance gap increases with the number of S-D pairs.
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paper_title: Dynamic Right-Sizing for Power-Proportional Data Centers

paper_content:

Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of low load. This paper investigates how much can be saved by dynamically "right-sizing" the data center by turning off servers during such periods and how to achieve that saving via an online algorithm. We propose a very general model and prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new "lazy" online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data-center workloads and show that significant cost savings are possible. Additionally, we contrast this new algorithm with the more traditional approach of receding horizon control.
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paper_title: Online algorithms for geographical load balancing

paper_content:

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective.
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paper_title: A Lightweight Message Authentication Scheme for Smart Grid Communications

paper_content:

Smart grid (SG) communication has recently received significant attentions to facilitate intelligent and distributed electric power transmission systems. However, communication trust and security issues still present practical concerns to the deployment of SG. In this paper, to cope with these challenging concerns, we propose a lightweight message authentication scheme features as a basic yet crucial component for secure SG communication framework. Specifically, in the proposed scheme, the smart meters which are distributed at different hierarchical networks of the SG can first achieve mutual authentication and establish the shared session key with Diffie-Hellman exchange protocol. Then, with the shared session key between smart meters and hash-based authentication code technique, the subsequent messages can be authenticated in a lightweight way. Detailed security analysis shows that the proposed scheme can satisfy the desirable security requirements of SG communications. In addition, extensive simulations have also been conducted to demonstrate the effectiveness of the proposed scheme in terms of low latency and few signal message exchanges.
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paper_title: Opportunities and Challenges of Software-Defined Mobile Networks in Network Security

paper_content:

Software-defined mobile network (SDMN) architecture integrates software-defined networks, network functions virtualization, and cloud computing principles in mobile networking environments to transform rigid and disparate legacy mobile networks into scalable and dynamic ecosystems. However, because SDMN architecture separates the control and data planes, it will significantly change the way security is managed and applied for mobile networks. In this article, the authors discuss the security challenges, vulnerabilities, and opportunities that need to be investigated and addressed for future SDMNs. It also highlights how common security threats in IP networks such as the Internet are now applicable in new open and IP-based SDMNs.
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paper_title: Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming

paper_content:

Cloud computing enables an economically promising paradigm of computation outsourcing. However, how to protect customers confidential data processed and generated during the computation is becoming the major security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computation than the general circuit representation. Specifically, by formulating private LP problem as a set of matrices/vectors, we develop efficient privacy-preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP and derive the necessary and sufficient conditions that correct results must satisfy. Such result verification mechanism is very efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.
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paper_title: Security and Privacy Issues of Fog Computing: A Survey

paper_content:

Fog computing is a promising computing paradigm that extends cloud computing to the edge of networks. Similar to cloud computing but with distinct characteristics, fog computing faces new security and privacy challenges besides those inherited from cloud computing. In this paper, we have surveyed these challenges and corresponding solutions in a brief manner.
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paper_title: Non-interactive verifiable computing: Outsourcing computation to untrusted workers

paper_content:

We introduce and formalize the notion of Verifiable Computation, which enables a computationally weak client to "outsource" the computation of a function F on various dynamically-chosen inputs x1, ...,xk to one or more workers. The workers return the result of the function evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out correctly on the given value xi. The primary constraint is that the verification of the proof should require substantially less computational effort than computing F(i) from scratch. ::: ::: We present a protocol that allows the worker to return a computationally-sound, non-interactive proof that can be verified in O(mċpoly(λ)) time, where m is the bit-length of the output of F, and λ is a security parameter. The protocol requires a one-time pre-processing stage by the client which takes O(|C|ċpoly(λ)) time, where C is the smallest known Boolean circuit computing F. Unlike previous work in this area, our scheme also provides (at no additional cost) input and output privacy for the client, meaning that the workers do not learn any information about the xi or yi values.
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paper_title: An optimization of security and trust management in distributed systems

paper_content:

With the development of cloud computing, the security and trust management in distributed systems is changeable. This paper proposes an architecture and solution of security and trust management for distributed systems which use cloud computing. Since the solution requires future technologies, an optimization to the security and trust management, including multi-paths transmission, virtual personal networks and encryption, is proposed. This optimization will enhance the security and trust in distributed systems.
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paper_title: Attribute-based authenticated key exchange

paper_content:

We introduce the concept of attribute-based authenticated key exchange (AB-AKE) within the framework of ciphertext-policy attribute-based systems. A notion of AKE-security for AB-AKE is presented based on the security models for group key exchange protocols and also taking into account the security requirements generally considered in the ciphertext-policy attribute-based setting. We also introduce a new primitive called encapsulation policy attribute-based key encapsulation mechanism (EP-AB-KEM) and then define a notion of chosen ciphertext security for EP-AB-KEMs. A generic one-round AB-AKE protocol that satisfies our AKE-security notion is then presented. The protocol is generically constructed from any EP-AB-KEM that achieves chosen ciphertext security. Finally, we propose an EP-AB-KEM from an existing attribute-based encryption scheme and show that it achieves chosen ciphertext security in the generic group and random oracle models. Instantiating our AB-AKE protocol with this EP-AB-KEM will result in a concrete one-round AB-AKE protocol also secure in the generic group and random oracle models.
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paper_title: OCP: A protocol for secure communication in federated content networks

paper_content:

Abstract Content Distribution Networks (CDNs) are networks that make intense use of caching and multicast overlay techniques for transmitting video and other stream media, a type of traffic that has been growing tremendously in the last years. To cope with this increasing demand, several telecommunications companies have been associated to create federated CDNs (FCDNs), that involves many different providers and, hence, distinct domains. Although beneficial in terms of increased capillarity and scalability of service delivery, the interaction between FCDN elements from different providers brings many new challenges. Among them, one that has received little attention so far refers to security, an essential service for preventing the misuse of the FCDN resources. Aiming to tackle this issue, this paper presents the Overlay Communication Protocol (OCP), a security mechanism that allows the secure signaling communication in FCDNs. OCP takes into account all elements involved in the content delivery process, addressing Route Forgery attacks and concealing the network structure from potential attackers. Together with the protocol description and security analysis, we also present experimental results on its implementation, showing that it introduces little impact on the overall network performance.
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paper_title: A survey on security in network functions virtualization

paper_content:

Network functions virtualization (NFV) is an emerging network technology. Instead of deploying hardware equipments for each network functions, virtualized network functions in NFV are realized through virtual machines (VMs) running various software on top of industry standard high volume servers or cloud computing infrastructure. NFV decreases hardware equipment costs and energy consumption, improves operational efficiency and optimizes network configuration. However, potential security issues is a major concern of NFV. In this paper, we survey the challenges and opportunities in NFV security. We describe the NFV architecture design and some potential NFV security issues and challenges. We also present existing NFV security solutions and products. We also survey NFV security use cases and explore promising research directions in this area.
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paper_title: Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

paper_content:

Abstract For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfill these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalized; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.
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paper_title: Introducing Connected Vehicles [Connected Vehicles]

paper_content:

The term connected vehicles refers to applications, services, and technologies that connect a vehicle to its surroundings. Adopting a definition similar to that of AUTO Connected Car News, a connected vehicle is basically the presence of devices in a vehicle that connect to other devices within the same vehicle and/or devices, networks, applications, and services outside the vehicle. Applications include everything from traffic safety and efficiency, infotainment, parking assistance, roadside assistance, remote diagnostics, and telematics to autonomous self-driving vehicles and global positioning systems (GPS). Typically, vehicles that include interactive advanced driver-assistance systems (ADASs) and cooperative intelligent transport systems (C-ITS) can be regarded as connected. Connected-vehicle safety applications are designed to increase situation awareness and mitigate traffic accidents through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. ADAS technology can be based on vision/camera systems, sensor technology, vehicle data networks, V2V, or V2I systems. Features may include adaptive cruise control, automate braking, incorporate GPS and traffic warnings, connect to smartphones, alert the driver to hazards, and keep the driver aware of what is in the blind spot. V2V communication technology could mitigate traffic collisions and improve traffic congestion by exchanging basic safety information such as location, speed, and direction between vehicles within range of each other. It can supplement active safety features, such as forward collision warning and blind-spot detection.
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paper_title: The Tactile Internet: Applications and Challenges

paper_content:

Wireless communications today enables us to connect devices and people for an unprecedented exchange of multimedia and data content. The data rates of wireless communications continue to increase, mainly driven by innovation in electronics. Once the latency of communication systems becomes low enough to enable a round-trip delay from terminals through the network back to terminals of approximately 1 ms, an overlooked breakthrough?human tactile to visual feedback control?will change how humans communicate around the world. Using these controls, wireless communications can be the platform for enabling the control and direction of real and virtual objects in many situations of our life. Almost no area of the economy will be left untouched, as this new technology will change health care, mobility, education, manufacturing, smart grids, and much more. The Tactile Internet will become a driver for economic growth and innovation and will help bring a new level of sophistication to societies.
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paper_title: Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics

paper_content:

Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers an operator by automating the object detection and classification process from recorded video streams. An operator only specifies an analysis criteria and duration of video streams to analyse. The streams are then fetched from a cloud storage, decoded and analysed on the cloud. The framework executes compute intensive parts of the analysis to GPU powered servers in the cloud. Vehicle and face detection are presented as two case studies for evaluating the framework, with one month of data and a 15 node cloud. The framework reliably performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled deployment of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least twice as fast than the cloud deployment without GPUs.
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Title: Mobile Edge Computing: Survey and Research Outlook
Section 1: INTRODUCTION
Description 1: This section introduces the concept of Mobile Edge Computing (MEC), its significance, and its emergence as an important technology for next-generation internet applications.

Section 2: Mobile Computing for 5G: From Clouds to Edges
Description 2: This section discusses the transition from cloud computing to edge computing in the context of 5G wireless systems, examining the limitations of mobile cloud computing and the advantages of MEC.

Section 3: Paper Motivation and Outline
Description 3: This section presents the motivation behind the paper and provides an outline of the key topics and structure of the paper.

Section 4: MEC COMPUTATION AND COMMUNICATION MODELS
Description 4: This section introduces the system models for key computation/communication components in a typical MEC system, including task models, communication models, and computation models for mobile devices and MEC servers.

Section 5: RESOURCE MANAGEMENT IN MEC SYSTEMS
Description 5: This section provides a comprehensive overview of resource management techniques in MEC systems, focusing on joint radio-and-computational resource allocation, MEC server scheduling, and cooperative edge computing systems.

Section 6: Single-User MEC Systems
Description 6: This section reviews research efforts focused on single-user MEC systems, discussing different task models and optimization solutions for energy and latency minimization.

Section 7: Multiuser MEC Systems
Description 7: This section considers resource management in multiuser MEC systems, addressing challenges like resource allocation, server scheduling, and cooperative computing among multiple mobile devices.

Section 8: MEC Systems with Heterogeneous Servers
Description 8: This section explores the role of heterogeneous MEC systems, discussing multi-level cloud interactions, server selection, cooperation, and computation migration.

Section 9: Challenges
Description 9: This section outlines critical research challenges that need to be addressed in future MEC studies, including two-timescale resource management, online task partitioning, and large-scale optimization.

Section 10: AN OUTLOOK FOR MEC RESEARCH
Description 10: This section discusses emerging research directions and identifies technical challenges and opportunities in MEC deployment, cache-enabled MEC, mobility management, green MEC, and security-and-privacy issues.

Section 11: Deployment of MEC Systems
Description 11: This section discusses the site selection for MEC servers, architecture design, and server density planning, emphasizing cost-effective and efficient deployment strategies.

Section 12: Cache-Enabled MEC
Description 12: This section explores the integration of caching with MEC, discussing service caching, data caching for MEC data analytics, and performance improvement strategies.

Section 13: Mobility Management for MEC
Description 13: This section addresses the challenges and solutions related to user mobility in MEC systems, including online prefetching, D2D communications, fault-tolerant MEC, and server scheduling.

Section 14: Green MEC
Description 14: This section investigates techniques for reducing the energy consumption of MEC systems, including dynamic right-sizing, geographical load balancing, and utilization of renewable energy.

Section 15: Security and Privacy Issues in MEC
Description 15: This section discusses the security and privacy challenges in MEC systems, focusing on trust, authentication, networking security, and secure computation mechanisms.

Section 16: STANDARDIZATION EFFORTS AND USE SCENARIOS OF MEC
Description 16: This section reviews the standardization efforts by ETSI for MEC, details the referenced MEC server framework, and elaborates on typical use scenarios for MEC applications.

Section 17: CONCLUSION
Description 17: This section provides concluding remarks, summarizing the key discussions and insights presented in the paper and underscoring the importance and potential directions for future MEC research.