Green Data Centers: A Survey, Perspectives, and Future Directions
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paper_title: Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing

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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: It's not easy being green

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Large-scale Internet applications, such as content distribution networks, are deployed across multiple datacenters and consume massive amounts of electricity. To provide uniformly low access latencies, these datacenters are geographically distributed and the deployment size at each location reflects the regional demand for the application. Consequently, an application's environmental impact can vary significantly depending on the geographical distribution of end-users, as electricity cost and carbon footprint per watt is location specific. In this paper, we describe FORTE: Flow Optimization based framework for request-Routing and Traffic Engineering. FORTE dynamically controls the fraction of user traffic directed to each datacenter in response to changes in both request workload and carbon footprint. It allows an operator to navigate the three-way tradeoff between access latency, carbon footprint, and electricity costs and to determine an optimal datacenter upgrade plan in response to increases in traffic load. We use FORTE to show that carbon taxes or credits are impractical in incentivizing carbon output reduction by providers of large-scale Internet applications. However, they can reduce carbon emissions by 10% without increasing the mean latency nor the electricity bill.
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paper_title: Models and Metrics to Enable Energy-Efficiency Optimizations

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Power consumption and energy efficiency are important factors in the initial design and day-to-day management of computer systems. Researchers and system designers need benchmarks that characterize energy efficiency to evaluate systems and identify promising new technologies. To predict the effects of new designs and configurations, they also need accurate methods of modeling power consumption.
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paper_title: Traffic merging for energy-efficient datacenter networks

paper_content:

Numerous studies have shown that datacenter networks typically see loads of between 5% – 25% but the energy draw of these networks is equal to operating them at maximum load. In this paper, we propose a novel way to make these networks more energy proportional - that is, the energy draw scales with the network load. We propose the idea of traffic aggregation, in which low traffic from N links is combined together to create H < N streams of high traffic. These streams are fed to H switch interfaces which run at maximum rate while the remaining interfaces are switched to the lowest possible one. We show that this merging can be accomplished with minimal latency and energy costs (less than 0.1W) while simultaneously allowing us a deterministic way of switching link rates between maximum and minimum. Using simulations based on previously developed traffic models, we show that 49% energy savings are obtained for 5% of the load while we get an energy savings of 22% for a 50% load. Hence, forasmuch as the packet losses are statistically insignificant, the results show that energy-proportional datacenter networks are indeed possible.
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paper_title: Energy proportional datacenter networks

paper_content:

Numerous studies have shown that datacenter computers rarely operate at full utilization, leading to a number of proposals for creating servers that are energy proportional with respect to the computation that they are performing. In this paper, we show that as servers themselves become more energy proportional, the datacenter network can become a significant fraction (up to 50%) of cluster power. In this paper we propose several ways to design a high-performance datacenter network whose power consumption is more proportional to the amount of traffic it is moving -- that is, we propose energy proportional datacenter networks. We first show that a flattened butterfly topology itself is inherently more power efficient than the other commonly proposed topology for high-performance datacenter networks. We then exploit the characteristics of modern plesiochronous links to adjust their power and performance envelopes dynamically. Using a network simulator, driven by both synthetic workloads and production datacenter traces, we characterize and understand design tradeoffs, and demonstrate an 85% reduction in power --- which approaches the ideal energy-proportionality of the network. Our results also demonstrate two challenges for the designers of future network switches: 1) We show that there is a significant power advantage to having independent control of each unidirectional channel comprising a network link, since many traffic patterns show very asymmetric use, and 2) system designers should work to optimize the high-speed channel designs to be more energy efficient by choosing optimal data rate and equalization technology. Given these assumptions, we demonstrate that energy proportional datacenter communication is indeed possible.
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paper_title: The cost of a cloud: research problems in data center networks

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The data centers used to create cloud services represent a significant investment in capital outlay and ongoing costs. Accordingly, we first examine the costs of cloud service data centers today. The cost breakdown reveals the importance of optimizing work completed per dollar invested. Unfortunately, the resources inside the data centers often operate at low utilization due to resource stranding and fragmentation. To attack this first problem, we propose (1) increasing network agility, and (2) providing appropriate incentives to shape resource consumption. Second, we note that cloud service providers are building out geo-distributed networks of data centers. Geo-diversity lowers latency to users and increases reliability in the presence of an outage taking out an entire site. However, without appropriate design and management, these geo-diverse data center networks can raise the cost of providing service. Moreover, leveraging geo-diversity requires services be designed to benefit from it. To attack this problem, we propose (1) joint optimization of network and data center resources, and (2) new systems and mechanisms for geo-distributing state.
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paper_title: Network traffic characteristics of data centers in the wild

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Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of data centers today. In this paper, we conduct an empirical study of the network traffic in 10 data centers belonging to three different categories, including university, enterprise campus, and cloud data centers. Our definition of cloud data centers includes not only data centers employed by large online service providers offering Internet-facing applications but also data centers used to host data-intensive (MapReduce style) applications). We collect and analyze SNMP statistics, topology and packet-level traces. We examine the range of applications deployed in these data centers and their placement, the flow-level and packet-level transmission properties of these applications, and their impact on network and link utilizations, congestion and packet drops. We describe the implications of the observed traffic patterns for data center internal traffic engineering as well as for recently proposed architectures for data center networks.
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paper_title: Energy-efficient algorithms

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Algorithmic solutions can help reduce energy consumption in computing environs.
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paper_title: Scheduling for speed bounded processors

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We consider online scheduling algorithms in the dynamic speedscaling model, where a processor can scale its speed between 0 andsome maximum speed T. The processor uses energy at ratesαwhen run at speed s,where α> 1 is a constant. Most modern processorsuse dynamic speed scaling to manage their energy usage. This leadsto the problem of designing execution strategies that are bothenergy efficient, and yet have almost optimum performance. ::: ::: We consider two problems in this model and give essentiallyoptimum possible algorithms for them. In the first problem, jobswith arbitrary sizes and deadlines arrive online and the goal is tomaximize the throughput, i.e. the total size of jobs completedsuccessfully. We give an algorithm that is 4-competitive forthroughput and O(1)-competitive for the energy used. Thisimproves upon the 14 throughput competitive algorithm of Chan etal. [10]. Our throughput guarantee is optimal as any onlinealgorithm must be at least 4-competitive even if the energy concernis ignored [7]. In the second problem, we consider optimizing thetrade-off between the total flow time incurred and the energyconsumed by the jobs. We give a 4-competitive algorithm to minimizetotal flow time plus energy for unweighted unit size jobs, and a (2+ o(1)) α/ln α-competitivealgorithm to minimize fractional weighted flow time plus energy.Prior to our work, these guarantees were known only when theprocessor speed was unbounded (T= ∞) [4].
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paper_title: Discrete Min-Energy Scheduling on Restricted Parallel Processors

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Different from the previous work on energy-efficient algorithms, which focused on assumption that a task can be assigned to any processor, we study the problem of task Scheduling with the objective of Energy Minimization on Restricted Parallel Processors (SEMRPP). Restriction accounts for affinities between tasks and processors, that is, a task has its own eligible processing set of processors. It assumes all tasks have a prescribed deadline on the execution time. We study the processors run at a finite number of distinct speeds, and the processors cannot change its speed during the computation of a task. Our work is motivated by the practical variable voltage processors that they cannot run at arbitrary speed and the task may be failure if the processor adjusts its speed during the computation of the task. We assess the complexity of the problem and present a polynomial time approximation algorithm with a bounded factor related to the adjacent speed ratio.
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paper_title: Power-aware scheduling for makespan and flow

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We consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric. For makespan, we give linear-time algorithms to compute all non-dominated solutions for the general uniprocessor problem and for the multiprocessor problem when every job requires the same amount of work. We also show that the multiprocessor problem becomes NP-hard when jobs can require different amounts of work.For total flow, we show that the optimal flow corresponding to a particular energy budget cannot be exactly computed on a machine supporting arithmetic and the extraction of roots. This hardness result holds even when scheduling equal-work jobs on a uniprocessor. We do, however, extend previous work by Pruhs et al. to give an arbitrarilygood approximation for scheduling equal-work jobs on a multiprocessor.
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paper_title: Speed Scaling of Tasks with Precedence Constraints

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We consider the problem of speed scaling to conserve energy in a multiprocessor setting where there are precedence constraints between tasks, and where the performance measure is the makespan. That is, we consider an energy bounded version of the classic problem Pm|prec|Cmax . We extend the standard 3-field notation and denote this problem as Sm|prec, energy|Cmax . We show that, without loss of generality, one need only consider constant power schedules. We then show how to reduce this problem to the problem Qm|prec|Cmax to obtain a poly-log(m)-approximation algorithm.
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paper_title: Routing for power minimization in the speed scaling model

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We study network optimization that considers power minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a network-wide optimization. Specifically, we study a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing power consumption. Optimizing the routes critically relies on the characteristic of the speed-power curve f(s), which is how power is consumed as a function of the processing speed s. If f is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the well-known logarithmic approximation for subadditive functions. However, for common speed-power curves such as polynomials f(s) = μsα, we are able to show a constant approximation via a simple scheme of randomized rounding. We also generalize this rounding approach to handle the case in which a nonzero startup cost σ appears in the speed-power curve, i.e., f(s) = {σ + μsα, if s >; 0; 0, if s = 0. We present an O((σ/μ)1/α)-approximation, and we discuss why coming up with an approximation ratio independent of the startup cost may be hard. Finally, we provide simulation results to validate our algorithmic approaches.
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paper_title: From preemptive to non-preemptive speed-scaling scheduling

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We are given a set of jobs, each one specified by its release date, its deadline and its processing volume (work), and a single (or a set of) speed-scalable processor(s). We adopt the standard model in speed-scaling in which if a processor runs at speed s then the energy consumption is s α units of energy per time unit, where α 1 is a small constant. Our goal is to find a schedule respecting the release dates and the deadlines of the jobs so that the total energy consumption to be minimized. While most previous works have studied the preemptive case of the problem, where a job may be interrupted and resumed later, we focus on the non-preemptive case where once a job starts its execution, it has to continue until its completion without any interruption. As the preemptive case is known to be polynomially solvable for both the single-processor and the multiprocessor case, we explore the idea of transforming an optimal preemptive schedule to a non-preemptive one. We prove that the preemptive optimal solution does not preserve enough of the structure of the non-preemptive optimal solution, and more precisely that the ratio between the energy consumption of an optimal non-preemptive schedule and the energy consumption of an optimal preemptive schedule can be very large even for the single-processor case. Then, we focus on some interesting families of instances: (i) equal-work jobs on a single-processor, and (ii) agreeable instances in the multiprocessor case. In both cases, we propose constant factor approximation algorithms. In the latter case, our algorithm improves the best known algorithm of the literature. Finally, we propose a (non-constant factor) approximation algorithm for general instances in the multiprocessor case.
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paper_title: Incorporating Rate Adaptation Into Green Networking for Future Data Centers

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Despite some proposals for energy-efficient topologies, most of the studies for saving energy in data center networks are focused on traffic engineering, i.e., consolidating flows and switching off unnecessary network devices. The major weakness of this approach is network oscillation brought by the frequent change of network topology when traffic fluctuates very fast. In this paper, we propose to incorporate rate adaptation into green data center networks. With rate adaptive network devices, we aim at approaching network-wide energy proportionality by routing optimization. We formalize the problem with an integer program and propose an efficient approximation algorithm - TSRR, solving the problem quickly while guaranteeing a constant performance ratio. Extensive range of simulations confirm that more than 40% of the energy can be saved while introducing very slight stretch on network delay.
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paper_title: A scheduling model for reduced CPU energy

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The energy usage of computer systems is becoming an important consideration, especially for battery-operated systems. Various methods for reducing energy consumption have been investigated, both at the circuit level and at the operating systems level. In this paper, we propose a simple model of job scheduling aimed at capturing some key aspects of energy minimization. In this model, each job is to be executed between its arrival time and deadline by a single processor with variable speed, under the assumption that energy usage per unit time, P, is a convex function, of the processor speed s. We give an off-line algorithm that computes, for any set of jobs, a minimum-energy schedule. We then consider some on-line algorithms and their competitive performance for the power function P(s)=s/sup p/ where p/spl ges/2. It is shown that one natural heuristic, called the Average Rate heuristic, uses at most a constant times the minimum energy required. The analysis involves bounding the largest eigenvalue in matrices of a special type.
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paper_title: On Arbitrating the Power-Performance Tradeoff in SaaS Clouds

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In this paper, we present an analytical framework for characterizing and optimizing the power-performance tradeoff in Software-as-a-Service (SaaS) cloud platforms. Our objectives are two-folded: 1) We maximize the operating revenue when serving heterogeneous SaaS applications with unpredictable user requests. 2) We minimize the power consumption when processing the user requests. To achieve these objectives, we construct a unified profit-maximizing objective to jointly consider revenue and cost in an economic view. An offline solution to maximize the supreme bound of the objective is first developed, to 1) justify the validity of our theoretical model, and 2) establish a benchmark to examine the effectiveness of other control solutions. As a highlight of our contributions, we take advantage of the Lyapunov optimization techniques to design and analyze an optimal yet practical control framework, which makes online decisions on request admission control, routing, and virtual machine (VMs) scheduling. Our control framework can accommodate a variety of design choices and operational requirements in a datacenter. Specifically, buffering facilities can be introduced to alleviate the bursty admitted requests and to improve the robustness of the system, and a power budget can be enforced to improve the datacenter performance (dollar) per watt. Our mathematical analyses and simulations have demonstrated both the optimality (in terms of the cost-effective power-performance tradeoff) and stability (in terms of robustness and adaptivity to time-varying and bursty user requests) achieved by our proposed control framework.
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paper_title: Optimal power allocation in server farms

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Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance. By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter. We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases.
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paper_title: The Bell Is Ringing in Speed-Scaled Multiprocessor Scheduling

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This paper investigates the problem of scheduling jobs on multiple speed-scaled processors, i.e., we have constant ?>1 such that running a processor at speed s results in energy consumption s ? per time unit. We consider the general case where each job has a monotonously increasing cost function that penalizes delay. This includes the so far considered cases of deadlines, flow time, and weighted flow time. For any type of delay cost functions, we obtain the following results: Any β-approximation algorithm for a single processor yields a randomized βB ? -approximation algorithm for multiple processors, where B ? is the ?th Bell number, that is, the number of partitions of a set of size ?. The generated schedule is without migration, but we compare it to an optimal schedule with migration. Hence, this result holds for migratory and non-migratory schedules. Analogously, we show that any β-competitive online algorithm for a single processor yields a βB ? -competitive online algorithm for multiple processors. Finally, we show that any β-approximation algorithm for multiple processors with migration yields a deterministic βB ? -approximation algorithm for multiple processors without migration. These facts improve several approximation ratios and lead to new results. For instance, we obtain the first constant factor online and offline approximation algorithm for multiple processors without migration for arbitrary release times, deadlines, and job sizes.
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paper_title: Energy-efficient algorithms for non-preemptive speed-scaling

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We improve complexity bounds for energy-efficient non-preemptive scheduling problems for both the single processor and multi-processor cases. As energy conservation has become a major concern, traditional scheduling problems have been revisited in the past few years to take into account the energy consumption [1]. We consider the speed scaling setting introduced by Yao et al. [20] where a set of jobs, each with a release date, deadline and work volume, are to be scheduled on a set of identical processors. The processors may change speed as a function of time and the energy they consume is the \(\alpha \)th power of their speed integrated over time. The objective is then to find a feasible non-preemptive schedule which minimizes the total energy used.
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paper_title: Energy Efficient Scheduling and Routing via Randomized Rounding

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We propose a unifying framework based on configuration linear programs and randomized rounding, for different energy optimization problems in the dynamic speed-scaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. We first consider the energy minimization problem of scheduling a set of jobs on a set of parallel speed scalable processors in a fully heterogeneous setting. For both the preemptive-non-migratory and the preemptive-migratory variants, our approach allows us to obtain solutions of almost the same quality as for the homogeneous environment. By exploiting the result for the preemptive-non-migratory variant, we are able to improve the best known approximation ratio for the single processor non-preemptive problem. Furthermore, we show that our approach allows to obtain a constant-factor approximation algorithm for the power-aware preemptive job shop scheduling problem. Finally, we consider the min-power routing problem where we are given a network modeled by an undirected graph and a set of uniform demands that have to be routed on integral routes from their sources to their destinations so that the energy consumption is minimized. We improve the best known approximation ratio for this problem.
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paper_title: Energy-efficient algorithms for flow time minimization

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We study scheduling problems in battery-operated computing devices, aiming at schedules with low total energy consumption. While most of the previous work has focused on finding feasible schedules in deadline-based settings, in this article we are interested in schedules that guarantee good response times. More specifically, our goal is to schedule a sequence of jobs on a variable-speed processor so as to minimize the total cost consisting of the energy consumption and the total flow time of all jobs. We first show that when the amount of work, for any job, may take an arbitrary value, then no online algorithm can achieve a constant competitive ratio. Therefore, most of the article is concerned with unit-size jobs. We devise a deterministic constant competitive online algorithm and show that the offline problem can be solved in polynomial time.
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paper_title: Energy proportional datacenter networks

paper_content:

Numerous studies have shown that datacenter computers rarely operate at full utilization, leading to a number of proposals for creating servers that are energy proportional with respect to the computation that they are performing. In this paper, we show that as servers themselves become more energy proportional, the datacenter network can become a significant fraction (up to 50%) of cluster power. In this paper we propose several ways to design a high-performance datacenter network whose power consumption is more proportional to the amount of traffic it is moving -- that is, we propose energy proportional datacenter networks. We first show that a flattened butterfly topology itself is inherently more power efficient than the other commonly proposed topology for high-performance datacenter networks. We then exploit the characteristics of modern plesiochronous links to adjust their power and performance envelopes dynamically. Using a network simulator, driven by both synthetic workloads and production datacenter traces, we characterize and understand design tradeoffs, and demonstrate an 85% reduction in power --- which approaches the ideal energy-proportionality of the network. Our results also demonstrate two challenges for the designers of future network switches: 1) We show that there is a significant power advantage to having independent control of each unidirectional channel comprising a network link, since many traffic patterns show very asymmetric use, and 2) system designers should work to optimize the high-speed channel designs to be more energy efficient by choosing optimal data rate and equalization technology. Given these assumptions, we demonstrate that energy proportional datacenter communication is indeed possible.
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paper_title: Speed scaling on parallel processors

paper_content:

In this paper we investigate algorithmic instruments leading to low powerconsumption in computing devices. While previous work on energy-efficient algorithms has mostly focused on single processor environments, in this paper we investigate multi-processor settings. We study the basic problem of scheduling a set of jobs, each specified by a release time, a deadline and a processing volume, on variable speed processors so as to minimize the total energy consumption. We first settle the complexity of speed scaling with unit size jobs. More specifically, we devise a polynomial time algorithm for agreeable deadlines and prove NP-hardness results for arbitrary release dates and deadlines. For the latter setting we also develop a polynomial time algorithm achieving a constant factor approximation guarantee that is independent of the number of processors. Additionally, we study speed scaling of jobs with arbitrary processing requirements and, again, develop constant factor approximation algorithms. We finally transform our offline algorithms into constant competitive online strategies.
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paper_title: Discrete and Continuous Min-Energy Schedules for Variable Voltage Processors ∗

paper_content:

Current dynamic voltage scaling techniques allow the speed of processors to be set dynamically to save energy consumption, which is a major concern in microprocessor design. A theoretical model for min-energy job scheduling was first proposed a decade ago, and it was shown that for any convex energy function, the min-energy schedule for a set of n jobs has a unique characterization and is computable in O(n3) time. This algorithm has remained as the most efficient known despite many investigations of this model. In this work, we give an algorithm with running time O(n2 log n) for finding the min-energy schedule. In contrast to the previous algorithm, which outputs optimal speed levels from high to low iteratively, our algorithm is based on finding successive approximations to the optimal schedule. At the core of the approximation is an efficient partitioning of the job set into high and low speed subsets by any speed threshold, without computing the exact speed function.
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paper_title: Non-preemptive speed scaling

paper_content:

We consider the following variant of the speed scaling problem introduced by Yao, Demers, and Shenker. We are given a set of jobs and we have a variable-speed processor to process them. The higher the processor speed, the higher the energy consumption. Each job is associated with its own release time, deadline, and processing volume. The objective is to find a feasible schedule that minimizes the energy consumption. Moreover, no preemption of jobs is allowed. ::: ::: Unlike the preemptive version that is known to be in P, the non-preemptive version of speed scaling is strongly NP-hard. In this work, we present a constant factor approximation algorithm for it. The main technical idea is to transform the problem into the unrelated machine scheduling problem with Lp-norm objective.
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paper_title: Reducing the Energy Consumption of Ethernet with Adaptive Link Rate (ALR)

paper_content:

The rapidly increasing energy consumption by computing and communications equipment is a significant economic and environmental problem that needs to be addressed. Ethernet network interface controllers (NICs) in the US alone consume hundreds of millions of US dollars in electricity per year. Most Ethernet links are underutilized and link energy consumption can be reduced by operating at a lower data rate. In this paper, we investigate adaptive link rate (ALR) as a means of reducing the energy consumption of a typical Ethernet link by adaptively varying the link data rate in response to utilization. Policies to determine when to change the link data rate are studied. Simple policies that use output buffer queue length thresholds and fine-grain utilization monitoring are shown to be effective. A Markov model of a state-dependent service rate queue with rate transitions only at service completion is used to evaluate the performance of ALR with respect to the mean packet delay, the time spent in an energy-saving low link data rate, and the oscillation of link data rates. Simulation experiments using actual and synthetic traffic traces show that an Ethernet link with ALR can operate at a lower data rate for over 80 percent of the time, yielding significant energy savings with only a very small increase in packet delay.
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paper_title: Power-Aware Speed Scaling in Processor Sharing Systems

paper_content:

Energy use of computer communication systems has quickly become a vital design consideration. One effective method for reducing energy consumption is dynamic speed scaling, which adapts the processing speed to the current load. This paper studies how to optimally scale speed to balance mean response time and mean energy consumption under processor sharing scheduling. Both bounds and asymptotics for the optimal speed scaling scheme are provided. These results show that a simple scheme that halts when the system is idle and uses a static rate while the system is busy provides nearly the same performance as the optimal dynamic speed scaling. However, the results also highlight that dynamic speed scaling provides at least one key benefit - significantly improved robustness to bursty traffic and mis-estimation of workload parameters.
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paper_title: Speed scaling to manage energy and temperature

paper_content:

Speed scaling is a power management technique that involves dynamically changing the speed of a processor. We study policies for setting the speed of the processor for both of the goals of minimizing the energy used and the maximum temperature attained. The theoretical study of speed scaling policies to manage energy was initiated in a seminal paper by Yao et al. [1995], and we adopt their setting. We assume that the power required to run at speed s is P(s) e sα for some constant α > 1. We assume a collection of tasks, each with a release time, a deadline, and an arbitrary amount of work that must be done between the release time and the deadline. Yao et al. [1995] gave an offline greedy algorithm YDS to compute the minimum energy schedule. They further proposed two online algorithms Average Rate (AVR) and Optimal Available (OA), and showed that AVR is 2α − 1 αα-competitive with respect to energy. We provide a tight αα bound on the competitive ratio of OA with respect to energy. We initiate the study of speed scaling to manage temperature. We assume that the environment has a fixed ambient temperature and that the device cools according to Newton's law of cooling. We observe that the maximum temperature can be approximated within a factor of two by the maximum energy used over any interval of length 1/b, where b is the cooling parameter of the device. We define a speed scaling policy to be cooling-oblivious if it is simultaneously constant-competitive with respect to temperature for all cooling parameters. We then observe that cooling-oblivious algorithms are also constant-competitive with respect to energy, maximum speed and maximum power. We show that YDS is a cooling-oblivious algorithm. In contrast, we show that the online algorithms OA and AVR are not cooling-oblivious. We then propose a new online algorithm that we call BKP. We show that BKP is cooling-oblivious. We further show that BKP is e-competitive with respect to the maximum speed, and that no deterministic online algorithm can have a better competitive ratio. BKP also has a lower competitive ratio for energy than OA for α ≥5. Finally, we show that the optimal temperature schedule can be computed offline in polynomial-time using the Ellipsoid algorithm.
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paper_title: An efficient algorithm for computing optimal discrete voltage schedules

paper_content:

We consider the problem of job scheduling on a variable voltage processor with d discrete voltage/speed levels. We give an algorithm which constructs a minimum energy schedule for n jobs in O(dnlog n) time. Previous approaches solve this problem by first computing the optimal continuous solution in O(n3) time and then adjusting the speed to discrete levels. In our approach, the optimal discrete solution is characterized and computed directly from the inputs. We also show that O(nlog n) time is required, hence the algorithm is optimal for fixed d.
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paper_title: A survey on Green communications using Adaptive Link Rate

paper_content:

The Information and Communication Technology sector is considered to be a major consumer of energy and has become an active participant in Green House Gas emissions. Lots of efforts have been devoted to make network infrastructure and network protocols power-aware and green. Among these efforts, Adaptive Link Rate (ALR) is one of the most widely discussed approaches. This survey highlights the most recent ALR approaches with a brief taxonomy and the state of the art.
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paper_title: Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey

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The theory of deterministic sequencing and scheduling has expanded rapidly during the past years. In this paper we survey the state of the art with respect to optimization and approximation algorithms and interpret these in terms of computational complexity theory. Special cases considered are single machine scheduling, identical, uniform and unrelated parallel machine scheduling, and open shop, flow shop and job shop scheduling. We indicate some problems for future research and include a selective bibliography.
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paper_title: Energy-Efficient Scheduling with Time and Processors Eligibility Restrictions

paper_content:

While previous work on energy-efficient algorithms focused on assumption that tasks can be assigned to any processor, we initially study the problem of task scheduling on restricted parallel processors. The objective is to minimize the overall energy consumption while speed scaling (SS) method is used to reduce energy consumption under the execution time constraint (Makespan Cmax). In this work, we discuss the speed setting in the continuous model that processors can run at arbitrary speed in [smin,smax]. The energy-efficient scheduling problem, involving task assignment and speed scaling, is inherently complicated as it is proved to be NP-Complete. We formulate the problem as an Integer Programming (IP) problem. Specifically, we devise a polynomial time optimal scheduling algorithm for the case tasks have an uniform size. Our algorithm runs in O(mn3logn) time, where m is the number of processors and n is the number of tasks. We then present a polynomial time algorithm that achieves an approximation factor of $2^{\alpha-1}(2-\frac{1}{m^{\alpha}})$ (α is the power parameter) when the tasks have arbitrary size work.
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paper_title: Enabling backbone networks to sleep

paper_content:

Today, backbone networks of telecom operators deploy a large number of devices and links. This is mainly due to both redundancy purposes for network service reliability and resource overdimensioning for maintaining quality of service during rush hours. Unfortunately, current network devices do not have power management primitives, and have constant energy consumption independent of their actual workloads. Starting from these considerations, we propose a viable approach to introduce and support standby modes in backbone network devices. This approach can be effectively used to almost halve the energy requirements of the whole telecom core network. Our main idea consists of periodically reconfiguring nodes and links to meet incoming traffic volumes and operational constraints of real-world networks, such as reliability, stability, quality of service, and reconvergence times. To this purpose, the approach we propose directly exploits the main features of both backbone device architectures and the network protocol stack.
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paper_title: Online Dynamic Power Management with Hard Real-Time Guarantees

paper_content:

We consider the problem of online dynamic power management that provides hard real-time guarantees for multi-processor systems. In this problem, a set of jobs, each associated with an arrival time, a deadline, and an execution time, arrives to the system in an online fashion. The objective is to compute a non-migrative preemptive schedule of the jobs and a sequence of power on/off operations of the processors so as to minimize the total energy consumption while ensuring that all the deadlines of the jobs are met. We assume that we can use as many processors as necessary. In this paper we examine the complexity of this problem and provide online strategies that lead to practical energy-efficient solutions for real-time multi-processor systems. ::: ::: First, we consider the case for which we know in advance that the set of jobs can be scheduled feasibly on a single processor. We show that, even in this case, the competitive factor of any online algorithm is at least 2.06. On the other hand, we give a 4-competitive online algorithm that uses at most two processors. For jobs with unit execution times, the competitive factor of this algorithm improves to 3.59. ::: ::: Second, we relax our assumption by considering as input multiple streams of jobs, each of which can be scheduled feasibly on a single processor. We present a trade-off between the energy-efficiency of the schedule and the number of processors to be used. More specifically, for k given job streams and h processors with h>k, we give a scheduling strategy such that the energy usage is at most 4.k/(h-k) times that used by any schedule which schedules each of the k streams on a separate processor. Finally, we drop the assumptions on the input set of jobs. We show that the competitive factor of any online algorithm is at least 2.28, even for the case of unit job execution times for which we further derive an O(1)-competitive algorithm.
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paper_title: Online strategies for dynamic power management in systems with multiple power-saving states

paper_content:

Online dynamic power management (DPM) strategies refer to strategies that attempt to make power-mode-related decisions based on information available at runtime. In making such decisions, these strategies do not depend upon information of future behavior of the system, or any a priori knowledge of the input characteristics. In this paper, we present online strategies, and evaluate them based on a measure called the competitive ratio that enables a quantitative analysis of the performance of online strategies. All earlier approaches (online or predictive) have been limited to systems with two power-saving states (e.g., idle and shutdown). The only earlier approaches that handled multiple power-saving states were based on stochastic optimization. This paper provides a theoretical basis for the analysis of DPM strategies for systems with multiple power-down states, without resorting to such complex approaches. We show how a relatively simple "online learning" scheme can be used to improve the competitive ratio over deterministic strategies using the notion of "probability-based" online DPM strategies. Experimental results show that the algorithm presented here attains the best competitive ratio in comparison with other known predictive DPM algorithms. The other algorithms that come close to matching its performance in power suffer at least an additional 40p wake-up latency on average. Meanwhile, the algorithms that have comparable latency to our methods use at least 25p more power on average.
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paper_title: Scheduling to minimize gaps and power consumption

paper_content:

This paper considers scheduling tasks while minimizing the power consumption of one or more processors, each of which can go to sleep at a fixed cost α. There are two natural versions of this problem, both considered extensively in recent work: minimize the total power consumption (including computation time), or minimize the number of "gaps" in execution. For both versions in a multiprocessor system, we develop a polynomial-time algorithm based on sophisticated dynamic programming. In a generalization of the power-saving problem, where each task can execute in any of a specified set of time intervals, we develop a (1 + 2 3 α)-approximation, and show that dependence on α is necessary. In contrast, the analogous multi-interval gap scheduling problem is set-cover hard (and thus not o(lg n)-approximable), even in the special cases of just two intervals per job or just three unit intervals per job. We also prove several other hardness-of-approximation results. Finally, we give an O(√n)-approximation for maximizing throughput given a hard upper bound on the number of gaps.
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paper_title: Energy-efficient data centers

paper_content:

Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide (\(\mathrm{CO}_2\)) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers.
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paper_title: Low complexity scheduling algorithms minimizing the energy for tasks with agreeable deadlines

paper_content:

Power management aims in reducing the energy consumed by computer systems while maintaining a good level of performance. One of the mechanisms used to save energy is the shut-down mechanism which puts the system into a sleep state when it is idle. No energy is consumed in this state, but a fixed amount of energy is required for a transition from the sleep state to the active state which is equal to L times the energy required for the execution of a unit-time task. In this paper, we focus on the off-line version of this problem where a set of unit-time tasks with release dates and deadlines have to be scheduled in order to minimize the overall consumed energy during the idle periods of the schedule. Here we focus on the case where the tasks have agreeable deadlines. For the single processor case, an O(n^3) algorithm has been proposed in Gururaj et al. (2010) for unit-time tasks and arbitrary L. We improve this result by introducing a new O(n^2) polynomial-time algorithm for tasks with arbitrary processing times and arbitrary L. For the multiprocessor case we also improve the complexity from O(n^3m^2) Gururaj et al. (2010) to O(n^2m) in the case of unit-time tasks and unit L.
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paper_title: Greening of the internet

paper_content:

In this paper we examine the somewhat controversial subject of energy consumption of networking devices in the Internet, motivated by data collected by the U.S. Department of Commerce. We discuss the impact on network protocols of saving energy by putting network interfaces and other router & switch components to sleep. Using sample packet traces, we first show that it is indeed reasonable to do this and then we discuss the changes that may need to be made to current Internet protocols to support a more aggressive strategy for sleeping. Since this is a position paper, we do not present results but rather suggest interesting directions for core networking research. The impact of saving energy is huge, particularly in the developing world where energy is a precious resource whose scarcity hinders widespread Internet deployment.
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paper_title: Polynomial-time algorithms for minimum energy scheduling

paper_content:

The aim of power management policies is to reduce the amount of energy consumed by computer systems while maintaining satisfactory level of performance. One common method for saving energy is to simply suspend the system during the idle times. No energy is consumed in the suspend mode. However, the process of waking up the system itself requires a certain fixed amount of energy, and thus suspending the system is beneficial only if the idle time is long enough to compensate for this additional energy expenditure. In the specific problem studied in the paper, we have a set of jobs with release times and deadlines that need to be executed on a single processor. Preemptions are allowed. The processor requires energy L to be woken up and, when it is on, it uses one unit of energy per one unit of time. It has been an open problem whether a schedule minimizing the overall energy consumption can be computed in polynomial time. We solve this problem in positive, by providing an O(n^5)-time algorithm. In addition we provide an O(n^4)-time algorithm for computing the minimum energy schedule when all jobs have unit length.
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paper_title: ElasticTree: Saving Energy in Data Center Networks

paper_content:

Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network's ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree, a network-wide power1 manager, which dynamically adjusts the set of active network elements -- links and switches--to satisfy changing data center traffic loads. ::: ::: We first compare multiple strategies for finding minimum-power network subsets across a range of traffic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production e-commerce website. Our results demonstrate that for data center workloads, ElasticTree can save up to 50% of network energy, while maintaining the ability to handle traffic surges. Our fast heuristic for computing network subsets enables ElasticTree to scale to data centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill.
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paper_title: Optimal Power-Down Strategies

paper_content:

We consider the problem of selecting threshold times to transition a device to low-power sleep states during an idle period. The two-state case, in which there is a single active and a single sleep state, is a continuous version of the ski-rental problem. We consider a generalized version in which there is more than one sleep state, each with its own power-consumption rate and transition costs. We give an algorithm that, given a system, produces a deterministic strategy whose competitive ratio is arbitrarily close to optimal. We also give an algorithm to produce the optimal online strategy given a system and a probability distribution that generates the length of the idle period. We also give a simple algorithm that achieves a competitive ratio of $3 + 2\sqrt{2} \approx 5.828$ for any system.
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paper_title: GreenTE: Power-aware traffic engineering

paper_content:

Current network infrastructures exhibit poor power efficiency, running network devices at full capacity all the time regardless of the traffic demand and distribution over the network. Most research on router power management are at component level or link level, treating routers as isolated devices. A complementary approach is to facilitate power management at network level by routing traffic through different paths to adjust the workload on individual routers or links. Given the high path redundancy and low link utilization in today's large networks, this approach can potentially allow more network devices or components to go into power saving mode. This paper proposes an intra-domain traffic engineering mechanism, GreenTE, which maximizes the number of links that can be put into sleep under given performance constraints such as link utilization and packet delay. Using network topologies and traffic data from several wide-area networks, our evaluation shows that GreenTE can reduce line-cards' power consumption by 27% to 42% under constraints that the maximum link utilization is below 50% and the network diameter remains the same as in shortest path routing.
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paper_title: Routing and Scheduling for Energy and Delay Minimization in the Powerdown Model

paper_content:

Energy conservation is drawing increasing attention in data networking. One school of thought believes that a dominant amount of energy saving comes from turning off network elements. The difficulty is that transitioning between the active and sleeping modes consumes considerable energy and time. This results in an obvious trade-off between saving energy and provisioning performance guarantees such as end-to-end delays. We study the following routing and scheduling problem in a network in which each network element either operates in the full-rate active mode or the zero-rate sleeping mode. For a given network and traffic matrix, routing determines the path along which each traffic stream traverses. For frame-based periodic scheduling, a schedule determines the active period per element within each frame and prioritizes packets within each active period. For a line topology, we present a schedule with close-to-minimum delay for a minimum active period per element. For an arbitrary topology, we partition the network into a collection of lines and utilize the near-optimal schedule along each line. Additional delay is incurred only when a path switches from one line to another. By minimizing the number of switchings via routing, we show a logarithmic approximation for both energy consumption and end-to-end delays. If routing is given as input, we present two schedules one of which has active period proportional to the traffic load per network element, and the other proportional to the maximum load over all elements. The end-to-end delay of the latter is much improved compared to the delay for the former. This demonstrates the trade-off between energy and delay.
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paper_title: Managing energy and server resources in hosting centers

paper_content:

Internet hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center operating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provision server resources for co-hosted services in a way that automatically adapts to offered load, improve the energy efficiency of server clusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs).Our system is based on an economic approach to managing shared server resources, in which services "bid" for resources as a function of delivered performance. The system continuously monitors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource availability, and can reduce server energy usage by 29% or more for a typical Web workload.
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paper_title: Cloud scheduling with setup cost

paper_content:

In this paper, we investigate the problem of online task scheduling of jobs such as MapReduce jobs, Monte Carlo simulations and generating search index from web documents, on cloud computing infrastructures. We consider the virtualized cloud computing setup comprising machines that host multiple identical virtual machines (VMs) under pay-as-you-go charging, and that booting a VM requires a constant setup time. The cost of job computation depends on the number of VMs activated, and the VMs can be activated and shutdown on demand. We propose a new bi-objective algorithm to minimize the maximum task delay, and the total cost of the computation. We study both the clairvoyant case, where the duration of each task is known upon its arrival, and the more realistic non-clairvoyant case.
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paper_title: Power Awareness in Network Design and Routing

paper_content:

Exponential bandwidth scaling has been a fundamental driver of the growth and popularity of the Internet. However, increases in bandwidth have been accompanied by increases in power consumption, and despite sustained system design efforts to address power demand, significant technological challenges remain that threaten to slow future bandwidth growth. In this paper we describe the power and associated heat management challenges in today's routers. We advocate a broad approach to addressing this problem that includes making power-awareness a primary objective in the design and configuration of networks, and in the design and implementation of network protocols. We support our arguments by providing a case study of power demands of two standard router platforms that enables us to create a generic model for router power consumption. We apply this model in a set of target network configurations and use mixed integer optimization techniques to investigate power consumption, performance and robustness in static network design and in dynamic routing. Our results indicate the potential for significant power savings in operational networks by including power-awareness.
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paper_title: Competitive randomized algorithms for nonuniform problems

paper_content:

Competitive analysis is concerned with comparing the performance of on-line algorithms with that of optimal off-line algorithms. In some cases randomization can lead to algorithms with improved performance ratios on worst-case sequences. In this paper we present new randomized on-line algorithms for snoopy caching and the spin-block problem. These algorithms achieve competitive ratios approachinge/(eź1) ź 1.58 against an oblivious adversary. These ratios are optimal and are a surprising improvement over the best possible ratio in the deterministic case, which is 2. We also consider the situation when the request sequences for these problems are generated according to an unknown probability distribution. In this case we show that deterministic algorithms that adapt to the observed request statistics also have competitive factors approachinge/(eź1). Finally, we obtain randomized algorithms for the 2-server problem on a class of isosceles triangles. These algorithms are optimal against an oblivious adversary and have competitive ratios that approache/(eź1). This compares with the ratio of 3/2 that can be achieved on an equilateral triangle.
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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: Sleep with Guilt and Work Faster to Minimize Flow plus Energy

paper_content:

In this paper we extend the study of flow-energy scheduling to a model that allows both sleep management and speed scaling. Our main result is a sleep management algorithm called IdleLonger, which works online for a processor with one or multiple levels of sleep states. The design of IdleLonger is interesting; among others, it may force the processor to idle or even sleep even though new jobs have already arrived. IdleLonger works in both clairvoyant and non-clairvoyant settings. We show how to adapt two existing speed scaling algorithms AJC [15] (clairvoyant) and LAPS [9] (non-clairvoyant) to the new model. The adapted algorithms, when coupled with IdleLonger, are shown to be O(1)-competitive clairvoyant and non-clairvoyant algorithms for minimizing flow plus energy on a processor that allows sleep management and speed scaling. ::: ::: The above results are based on the traditional model with no limit on processor speed. If the processor has a maximum speed, the problem becomes more difficult as the processor, once overslept, cannot rely on unlimited extra speed to catch up the delay. Nevertheless, we are able to enhance IdleLonger and AJC so that they remain O (1)-competitive for flow plus energy under the bounded speed model. Non-clairvoyant scheduling in the bounded speed model is left as an open problem.
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paper_title: Race to idle: New algorithms for speed scaling with a sleep state

paper_content:

We study an energy conservation problem where a variable-speed processor is equipped with a sleep state. Executing jobs at high speeds and then setting the processor asleep is an approach that can lead to further energy savings compared to standard dynamic speed scaling. We consider classical deadline-based scheduling, i.e. each job is specified by a release time, a deadline and a processing volume. For general convex power functions, Irani et al. [12] devised an offline 2-approximation algorithm. Roughly speaking, the algorithm schedules jobs at a critical speed Scrit that yields the smallest energy consumption while jobs are processed. For power functions P(s) = sα + γ, where s is the processor speed, Han et al. [11] gave an (αα + 2)-competitive online algorithm. ::: ::: We investigate the offline setting of speed scaling with a sleep state. First we prove NP-hardness of the optimization problem. Additionally, we develop lower bounds, for general convex power functions: No algorithm that constructs Scrit-schedules, which execute jobs at speeds of at least scrit, can achieve an approximation factor smaller than 2. Furthermore, no algorithm that minimizes the energy expended for processing jobs can attain an approximation ratio smaller than 2. ::: ::: We then present an algorithmic framework for designing good approximation algorithms. For general convex power functions, we derive an approximation factor of 4/3. For power functions P(s) = βsα + γ, we obtain an approximation of 137/117 < 1.171. We finally show that our framework yields the best approximation guarantees for the class of Scrit-schedules. For general convex power functions, we give another 2-approximation algorithm. For functions P(s) = βsα + γ, we present tight upper and lower bounds on the best possible approximation factor. The ratio is exactly eW−1(−e−1−1/e)/(eW−1(−e−1−1/e) + 1) < 1.211, where W--1 is the lower branch of the Lambert W function.
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paper_title: New Online Algorithm for Dynamic Speed Scaling with Sleep State

paper_content:

We consider an energy-efficient scheduling problem where n jobs J 1 , J 2 , ?, J n need to be executed such that the total energy usage by these jobs is minimized while ensuring that each job is finished within its deadline. The processor executing these jobs can be in either active or sleep state at any point of time. The speed of the processor in an active state can be arbitrary. If the processor is running at a speed s ? 0 , the required power P ( s ) is assumed to be equal to s α + g where α 1 , g 0 are constants. On the other hand, the required power is zero when the processor is in the sleep state. However, L 0 amount of wake-up energy is needed to wake-up the processor from the sleep state to the active state.In this paper, we work in an online setting where a job is known only at its arrival time, along with its processing volume and deadline. In such a setting, the currently best-known algorithm by Han et al. (2010) 3] provides a competitive ratio max ? { 4 , 2 + α α } of energy usage. We present a new online algorithm SqOA which provides a competitive ratio max ? { 4 , 2 + ( 2 - 1 / α ) α 2 α - 1 } of energy usage. For α ? 2.34 , the competitive ratio of our algorithm is better than that of any other existing algorithm for this problem.
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paper_title: Data centers power reduction: A two time scale approach for delay tolerant workloads

paper_content:

In this work we focus on a stochastic optimization based approach to make distributed routing and server management decisions in the context of large-scale, geographically distributed data centers, which offers significant potential for exploring power cost reductions. Our approach considers such decisions at different time scales and offers provable power cost and delay characteristics. The utility of our approach and its robustness are also illustrated through simulation-based experiments under delay tolerant workloads.
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paper_title: Energy-aware traffic engineering

paper_content:

Energy consumption of the Internet is already substantial and it is likely to increase as operators deploy faster equipment to handle popular bandwidth-intensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardware-based techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunction with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineering (EATe), a technique that takes energy consumption into account while achieving the same traffic rates as the energy-oblivious approaches. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over realistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe successfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, resulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep.
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paper_title: Algorithms for power savings

paper_content:

This article examines two different mechanisms for saving power in battery-operated embedded systems. The first strategy is that the system can be placed in a sleep state if it is idle. However, a fixed amount of energy is required to bring the system back into an active state in which it can resume work. The second way in which power savings can be achieved is by varying the speed at which jobs are run. We utilize a power consumption curve P(s) which indicates the power consumption level given a particular speed. We assume that P(s) is convex, nondecreasing, and nonnegative for s ≥ 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the first theoretical analysis of systems that can use both mechanisms. We give an offline algorithm that is within a factor of 2 of the optimal algorithm. We also give an online algorithm with a constant competitive ratio.
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paper_title: Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

paper_content:

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.
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paper_title: Deadline Scheduling and Power Management for Speed Bounded Processors ∗

paper_content:

In this paper we consider online deadline scheduling on a processor that can manage its energy usage by scaling the speed dynamically or entering a sleep state. A new online scheduling algorithm called SOA is presented. Assuming speed can be scaled arbitrarily high (the infinite speed model), SOA can complete all jobs with reduced energy usage, improving the competitive ratio for energy from 2^2^@a^-^[email protected]^@a+2^@a^-^1+2 (Irani et al. (2007) [17]) to @a^@a+2, where @a is the constant involved in the speed-to-power function, commonly believed to be 2 or 3. More importantly, SOA is the first algorithm that works well even if the processor has a fixed maximum speed and the system is overloaded. In this case, SOA is 4-competitive for throughput and (@a^@[email protected]^24^@a+2)-competitive for energy. Note that the throughput ratio cannot be better than 4 even if energy is not a concern.
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paper_title: Reducing Network Energy Consumption via Sleeping and Rate-Adaptation

paper_content:

We present the design and evaluation of two forms of power management schemes that reduce the energy consumption of networks. The first is based on putting network components to sleep during idle times, reducing energy consumed in the absence of packets. The second is based on adapting the rate of network operation to the offered workload, reducing the energy consumed when actively processing packets. ::: ::: For real-world traffic workloads and topologies and using power constants drawn from existing network equipment, we show that even simple schemes for sleeping or rate-adaptation can offer substantial savings. For instance, our practical algorithms stand to halve energy consumption for lightly utilized networks (10-20%). We show that these savings approach the maximum achievable by any algorithms using the same power management primitives. Moreover this energy can be saved without noticeably increasing loss and with a small and controlled increase in latency (<10ms). Finally, we show that both sleeping and rate adaptation are valuable depending (primarily) on the power profile of network equipment and the utilization of the network itself.
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paper_title: A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State

paper_content:

We study classical deadline-based preemptive scheduling of jobs in a computing environment equipped with both dynamic speed scaling and sleep state capabilities: Each job is specified by a release time, a deadline and a processing volume, and has to be scheduled on a single, speed-scalable processor that is supplied with a sleep state. In the sleep state, the processor consumes no energy, but a constant wake-up cost is required to transition back to the active state. In contrast to speed scaling alone, the addition of a sleep state makes it sometimes beneficial to accelerate the processing of jobs in order to transition the processor to the sleep state for longer amounts of time and incur further energy savings. The goal is to output a feasible schedule that minimizes the energy consumption. Since the introduction of the problem by Irani et al. [17], its exact computational complexity has been repeatedly posed as an open question (see e.g. [2,9,16]). The currently best known upper and lower bounds are a 4/3-approximation algorithm and NP-hardness due to [2] and [2, 18], respectively. ::: ::: We close the aforementioned gap between the upper and lower bound on the computational complexity of speed scaling with sleep state by presenting a fully polynomial-time approximation scheme for the problem. The scheme is based on a transformation to a non-preemptive variant of the problem, and a discretization that exploits a carefully defined lexicographical ordering among schedules.
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paper_title: SleepScale: runtime joint speed scaling and sleep states management for power efficient data centers

paper_content:

Power consumption in data centers has been growing significantly in recent years. To reduce power, servers are beingequipped with increasingly sophisticated power management mechanisms. Different mechanisms offer dramatically different trade-offs between power savings and performance penalties. Considering the complexity, variety, and temporallyvarying nature of the applications hosted in a typical data center, intelligently determining which power management policy to use and when is a complicated task. In this paper we analyze a system model featuring both performance scaling and low-power states. We reveal the interplay between performance scaling and low-power states via intensive simulation and analytic verification. Based on the observations, we present SleepScale, a runtime power management tool designed to efficiently exploit existing power control mechanisms. At run time, SleepScale characterizes power consumption and quality-of-service (QoS) for each lowpower state and frequency setting, and selects the best policy for a given QoS constraint. We evaluate SleepScale using workload traces from data centers and achieve significant power savings relative to conventional power management strategies
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paper_title: Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

paper_content:

This keynote paper: presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.
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paper_title: The Impact of Dynamically Heterogeneous Multicore Processors on Thread Scheduling

paper_content:

Although most current multicore processors are homogeneous, microarchitects are now proposing heterogeneous core implementations, including systems in which heterogeneity is introduced at runtime. This article shows that operating system schedulers must consider dynamic heterogeneity or suffer significant power-efficiency and performance losses.
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paper_title: Scheduling Heterogeneous Processors Isn ’ t As Easy As You Think

paper_content:

We consider preemptive online scheduling algorithms to minimize the total weighted/unweighted flow time plus energy for speed-scalable heterogeneous multiprocessors. We show that the well-known priority scheduling algorithms Highest Density First, Weighted Shortest Elapsed Time First, and Weighted Late Arrival Processor Sharing, are not O(1)-speed O(1)-competitive for the objective of weighted flow even in the special case of fixed variable speed processors (aka the related machines setting). This illustrates that scheduling heterogeneous multiprocessors is a different, and algorithmically more challenging problem, than scheduling homogeneous multiprocessors. ::: ::: We then show that a variation of the non-clairvoyant algorithm Late Arrival Processor Sharing coupled with a non-obvious speed scaling algorithm is scalable for the objective of unweighted flow plus energy on speed-scalable multiprocessors. This is the first provably scalable non-clairvoyant algorithm on heterogeneous multi-processors, even in the related machines setting, for the objective of total (unweighted) flow time.
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paper_title: Evaluating the impact of job scheduling and power management on processor lifetime for chip multiprocessors

paper_content:

Temperature-induced reliability issues are among the major challenges for multicore architectures. Thermal hot spots and thermal cycles combine to degrade reliability. This research presents new reliability-aware job scheduling and power management approaches for chip multiprocessors. Accurate evaluation of these policies requires a novel simulation framework that can capture architecture-level effects over tens of seconds or longer, while also capturing thermal interactions among cores resulting from dynamic scheduling policies. Using this framework and a set of new thermal management policies, this work shows that techniques that offer similar performance, energy, and even peak temperature can differ significantly in their effects on the expected processor lifetime.
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paper_title: Core architecture optimization for heterogeneous chip multiprocessors

paper_content:

Previous studies have demonstrated the advantages of single-ISA heterogeneous multi-core architectures for power and performance. However, none of those studies examined how to design such a processor; instead, they started with an assumed combination of pre-existing cores. This work assumes the flexibility to design a multi-core architecture from the ground up and seeks to address the following question: what should be the characteristics of the cores for a heterogeneous multi-processor for the highest area or power efficiency? The study is done for varying degrees of thread-level parallelism and for different area and power budgets. The most efficient chip multiprocessors are shown to be heterogeneous, with each core customized to a different subset of application characteristics — no single core is necessarily well suited to all applications. The performance ordering of cores on such processors is different for different applications; there is only a partial ordering among cores in terms of resources and complexity. This methodology produces performance gains as high as 40%. The performance improvements come with the added cost of customization.
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paper_title: Computer Networking: A Top-Down Approach

paper_content:

Certain data-communication protocols hog the spotlight, but all of them have a lot in common. Computer Networking: A Top-Down Approach Featuring the Internet explains the engineering problems that are inherent in communicating digital information from point to point. The top-down approach mentioned in the subtitle means that the book starts at the top of the protocol stack--at the application layer--and works its way down through the other layers, until it reaches bare wire. The authors, for the most part, shun the well-known seven-layer Open Systems Interconnection (OSI) protocol stack in favor of their own five-layer (application, transport, network, link, and physical) model. It's an effective approach that helps clear away some of the hand waving traditionally associated with the more obtuse layers in the OSI model. The approach is definitely theoretical--don't look here for instructions on configuring Windows 2000 or a Cisco router--but it's relevant to reality, and should help anyone who needs to understand networking as a programmer, system architect, or even administration guru.The treatment of the network layer, at which routing takes place, is typical of the overall style. In discussing routing, authors James Kurose and Keith Ross explain (by way of lots of clear, definition-packed text) what routing protocols need to do: find the best route to a destination. Then they present the mathematics that determine the best path, show some code that implements those algorithms, and illustrate the logic by using excellent conceptual diagrams. Real-life implementations of the algorithms--including Internet Protocol (both IPv4 and IPv6) and several popular IP routing protocols--help you to make the transition from pure theory to networking technologies. --David WallTopics covered: The theory behind data networks, with thorough discussion of the problems that are posed at each level (the application layer gets plenty of attention). For each layer, there's academic coverage of networking problems and solutions, followed by discussion of real technologies. Special sections deal with network security and transmission of digital multimedia.
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paper_title: VirtualPower: coordinated power management in virtualized enterprise systems

paper_content:

Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs `soft' versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption.
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paper_title: Risk management for virtual machines consolidation in data centers

paper_content:

Virtual machines (VMs) consolidation has emerged as an important method for the design of energy-efficient data centers. The purpose is to aggregate VMs to fewer physical machines and put the idle servers into power-saving mode. Existing researches mainly focus on transforming the VMs consolidation into various bin packing problems. However, VMs consolidation may cost Service Level Agreement (SLA) violations just after the migration due to the uncertainty of applications' demands. In this paper, we provide a SLA risk management framework, involving a stochastic program to solve the resource allocation for VMs and an algorithm for dynamic VMs consolidation at runtime, to optimize both the energy consumption saving and SLA violations. We validate the proposed algorithm using workloads from a real world system. The results compare with other VMs consolidation algorithms that without considering risk, and show that our SLA violations is reduced by four times from 25% to 2% – 5% while only losing little energy consumption saving.
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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: No "power" struggles: coordinated multi-level power management for the data center

paper_content:

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.
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paper_title: Entropy: a consolidation manager for clusters

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Clusters provide powerful computing environments, but in practice much of this power goes to waste, due to the static allocation of tasks to nodes, regardless of their changing computational requirements. Dynamic consolidation is an approach that migrates tasks within a cluster as their computational requirements change, both to reduce the number of nodes that need to be active and to eliminate temporary overload situations. Previous dynamic consolidation strategies have relied on task placement heuristics that use only local optimization and typically do not take migration overhead into account. However, heuristics based on only local optimization may miss the globally optimal solution, resulting in unnecessary resource usage, and the overhead for migration may nullify the benefits of consolidation. In this paper, we propose the Entropy resource manager for homogeneous clusters, which performs dynamic consolidation based on constraint programming and takes migration overhead into account. The use of constraint programming allows Entropy to find mappings of tasks to nodes that are better than those found by heuristics based on local optimizations, and that are frequently globally optimal in the number of nodes. Because migration overhead is taken into account, Entropy chooses migrations that can be implemented efficiently, incurring a low performance overhead.
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paper_title: A large-scale service system with packing constraints: minimizing the number of occupied servers

paper_content:

We consider a large-scale service system model proposed in [14], which is motivated by the problem of efficient placement of virtual machines to physical host machines in a network cloud, so that the total number of occupied hosts is minimized. Customers of different types arrive to a system with an infinite number of servers. A server packing configuration is the vector k = {ki}, where ki is the number of type-i customers that the server "contains". Packing constraints are described by a fixed finite set of allowed configurations. Upon arrival, each customer is placed into a server immediately, subject to the packing constraints; the server can be idle or already serving other customers. After service completion, each customer leaves its server and the system. It was shown in [14] that a simple real-time algorithm, called Greedy, is asymptotically optimal in the sense of minimizing ∑k Xk1+α in the stationary regime, as the customer arrival rates grow to infinity. (Here α > 0, and Xk denotes the number of servers with configuration k.) In particular, when parameter α is small, and in the asymptotic regime where customer arrival rates grow to infinity, Greedy solves a problem approximating one of minimizing ∑k Xk, the number of occupied hosts. In this paper we introduce the algorithm called Greedy with sublinear Safety Stocks (GSS), and show that it asymptotically solves the exact problem of minimizing ∑k Xk. An important feature of the algorithm is that sublinear safety stocks of Xk are created automatically - when and where necessary - without having to determine a priori where they are required. Moreover, we also provide a tight characterization of the rate of convergence to optimality under GSS. The GSS algorithm is as simple as Greedy, and uses no more system state information than Greedy does.
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paper_title: A simple on-line bin-packing algorithm

paper_content:

The one-dimensional on-line bin-packing problem is considered, A simple <italic>O</italic>(1)-space and <italic>O</italic>(<italic>n</italic>)-time algorithm, called HARMONIC<italic><subscrpt>M</subscrpt></italic>, is presented. It is shown that this algorithm can achieve a worst-case performance ratio of less than 1.692, which is better than that of the <italic>O</italic>(<italic>n</italic>)-space and <italic>O</italic>(<italic>n</italic> log <italic>n</italic>)-time algorithm FIRST FIT. Also shown is that 1.691 … is a lower bound for <italic>all</italic> <italic>0</italic>(1)-space on-line bin-packing algorithms. Finally a revised version of HARMONIC<italic><subscrpt>M </subscrpt></italic>, an <italic>O</italic>(<italic>n</italic>)-space and <italic>O</italic>(<italic>n</italic>)- time algorithm, is presented and is shown to have a worst-case performance ratio of less than 1.636. ::: d
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paper_title: Power and performance management of virtualized computing environments via lookahead control

paper_content:

There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
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paper_title: Consolidating virtual machines with dynamic bandwidth demand in data centers

paper_content:

Recent advances in virtualization technology have made it a common practice to consolidate virtual machines(VMs) into a fewer number of servers. An efficient consolidation scheme requires that VMs are packed tightly, yet receive resources commensurate with their demands. However, measurements from production data centers show that the network bandwidth demands of VMs are dynamic, making it difficult to characterize the demands by a fixed value and to apply traditional consolidation schemes. In this work, we formulate the VM consolidation into a Stochastic Bin Packing problem and propose an online packing algorithm by which the number of servers required is within equation of the optimum for any ∈ > 0. The result can be improved to within equation of the optimum in a special case. In addition, we use numerical experiments to evaluate the proposed consolidation algorithm and observe 30% server reduction compared to several benchmark algorithms.
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paper_title: Dynamic Placement of Virtual Machines for Managing SLA Violations

paper_content:

A dynamic server migration and consolidation algorithm is introduced. The algorithm is shown to provide substantial improvement over static server consolidation in reducing the amount of required capacity and the rate of service level agreement violations. Benefits accrue for workloads that are variable and can be forecast over intervals shorter than the time scale of demand variability. The management algorithm reduces the amount of physical capacity required to support a specified rate of SLA violations for a given workload by as much as 50% as compared to static consolidation approach. Another result is that the rate of SLA violations at fixed capacity may be reduced by up to 20%. The results are based on hundreds of production workload traces across a variety of operating systems, applications, and industries.
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paper_title: On the Complexity of Power Minimization Schemes in Data Center Networks

paper_content:

In this paper, we consider migration of virtual machines in a data center to minimize network power consumption. Network power is consumed when switches are turned on, and conserved when they are turned off; the optimization problem then is to site virtual machines within the data center to achieve connectivity and desired bandwidth while turning on as few switches as possible. Depending upon specifics of the permissible optimization, and topology considerations within the data center, this optimization problem can be easy, or hard. We fully taxonomize the suite of optimization problems in this general space, showing that the most complex problem (placement and routing of virtual machines in a topologically-rich data center network) is NP- hard. We offer a placement technique based on a classic VLSI placement algorithm, and demonstrate efficacy on a trace set derived from a production data center.
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paper_title: Joint power optimization of data center network and servers with correlation analysis

paper_content:

Data center power optimization has recently received a great deal of research attention. For example, server consolidation has been demonstrated as one of the most effective energy saving methodologies. Likewise, traffic consolidation has also been recently proposed to save energy for data center networks (DCNs). However, current research on data center power optimization focuses on servers and DCN separately. As a result, the optimization results are often inferior, because server consolidation without considering the DCN may cause traffic congestion and thus degraded network performance. On the other hand, server consolidation may change the DCN topology, allowing new opportunities for energy savings. In this paper, we propose PowerNetS, a power optimization strategy that leverages workload correlation analysis to jointly minimize the total power consumption of servers and the DCN. The design of PowerNetS is based on the key observations that the workloads of different servers and DCN traffic flows do not peak at exactly the same time. Thus, more energy savings can be achieved if the workload correlations are considered in server and traffic consolidations. In addition, PowerNetS considers the DCN topology during server consolidation, which leads to less inter-server traffic and thus more energy savings and shorter network delays. We implement PowerNetS on a hardware testbed composed of 10 virtual switches configured with a production 48port OpenFlow switch and 6 servers. Our empirical results with Wikipedia, Yahoo!, and IBM traces demonstrate that PowerNetS can save up to 51.6% of energy for a data center. PowerNetS also outperforms two state-of-the-art baselines by 44.3% and 15.8% on energy savings, respectively. Our simulation results with 72 switches and 122 servers also show the superior energy efficiency of PowerNetS over the baselines.
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paper_title: Joint VM placement and routing for data center traffic engineering

paper_content:

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: Joint Host-Network Optimization for Energy-Efficient Data Center Networking

paper_content:

Data centers consume significant amounts of energy. As severs become more energy efficient with various energy saving techniques, the data center network (DCN) has been accounting for 20% or more of the energy consumed by the entire data center. While DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns. The objective of this work is to improve the energy efficiency of DCNs during off-peak traffic time by powering off idle devices. Although there exist a number of energy optimization solutions for DCNs, they consider only either the hosts or network, but not both. In this paper, we propose a joint optimization scheme that simultaneously optimizes virtual machine (VM) placement and network flow routing to maximize energy savings, and we also build an OpenFlow based prototype to experimentally demonstrate the effectiveness of our design. First, we formulate the joint optimization problem as an integer linear program, but it is not a practical solution due to high complexity. To practically and effectively combine host and network based optimization, we present a unified representation method that converts the VM placement problem to a routing problem. In addition, to accelerate processing the large number of servers and an even larger number of VMs, we describe a parallelization approach that divides the DCN into clusters for parallel processing. Further, to quickly find efficient paths for flows, we propose a fast topology oriented multipath routing algorithm that uses depth-first search to quickly traverse between hierarchical switch layers and uses the best-fit criterion to maximize flow consolidation. Finally, we have conducted extensive simulations and experiments to compare our design with existing ones. The simulation and experiment results fully demonstrate that our design outperforms existing hostor network-only optimization solutions, and well approximates the ideal linear program.
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paper_title: Greening data center networks with throughput-guaranteed power-aware routing

paper_content:

Cloud based applications and services require high performance and strong reliability provided by data center networks. To overcome the problem of traditional tree based data center network, recently many new network architectures are proposed, such as Fat-Tree and BCube. They use aggressively over-provisioned network devices and links to achieve 1:1 oversubscription ratio. However, most of the time data center traffic is far below the peak value and a large number of idle network devices and links in data centers consume a significant amount of power, which is now becoming a big problem for many cloud providers.In this paper, we aim to reduce the power consumption of high-density data center networks from the routing perspective while meeting the network performance requirement. We call this kind of routing throughput-guaranteed power-aware routing. The essence of our idea is to use as little network power as possible to provide the routing service, without significantly compromise on the network performance. The idle network devices and links can be shut down or put into the sleep mode for power saving. Extensive simulations conducted in typical data center networks show that our power-aware routing can effectively reduce the power consumption of network devices, especially under low network loads.
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paper_title: Identifying and using energy-critical paths

paper_content:

The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energy-reduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimality-scalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.
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paper_title: HERO: Hierarchical energy optimization for data center networks

paper_content:

With the rapid growth of data center networks in providing a myriad of services and applications to store, access, and process data, the power consumption of data center networks has become critically important and must be efficiently managed. In this paper, we discuss the power optimization of data center networks in a hierarchical perspective. We establish a two-level power optimization model to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximum link utilization. After having shown that the addressed problem falls in the class of capacity constraint multi-commodity flow problems, we design several simple heuristic algorithms to solve the problem. Our simulations show that hierarchical energy optimization can effectively save power consumption in data center networks.
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paper_title: GreenDCN: a General Framework for Achieving Energy Efficiency in Data Center Networks

paper_content:

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.
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paper_title: CARPO: Correlation-aware power optimization in data center networks

paper_content:

Power optimization has become a key challenge in the design of large-scale enterprise data centers. Existing research efforts focus mainly on computer servers to lower their energy consumption, while only few studies have tried to address the energy consumption of data center networks (DCNs), which can account for 20% of the total energy consumption of a data center. In this paper, we propose CARPO, a correlation-aware power optimization algorithm that dynamically consolidates traffic flows onto a small set of links and switches in a DCN and then shuts down unused network devices for energy savings. In sharp contrast to existing work, CARPO is designed based on a key observation from the analysis of real DCN traces that the bandwidth demands of different flows do not peak at exactly the same time. As a result, if the correlations among flows are considered in consolidation, more energy savings can be achieved. In addition, CARPO integrates traffic consolidation with link rate adaptation for maximized energy savings. We implement CARPO on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 8 servers. Our empirical results with Wikipedia traces demonstrate that CARPO can save up to 46% of network energy for a DCN, while having only negligible delay increases. CARPO also outperforms two state-of-the-art baselines by 19.6% and 95% on energy savings, respectively. Our simulation results with 61 flows also show the superior energy efficiency of CARPO over the baselines.
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paper_title: Power budgeting for virtualized data centers

paper_content:

Power costs are very significant for data centers. To maximally utilize the provisioned power capacity, data centers often employ over-subscription, that is, the sum of peak consumptions of individual servers may be greater than the provisioned capacity. Power budgeting methods are employed to ensure that actual consumption never exceeds capacity. However, current power budgeting methods enforce capacity limits in hardware and are not well suited for virtualized servers because the hardware is shared among multiple applications. We present a power budgeting system for virtualized infrastructures that enforces power limits on individual distributed applications. Our system enables multiple applications to share the same servers but operate with their individual quality of service guarantees. It responds to workload and power availability changes, by dynamically allocating appropriate amount of power to different applications and tiers within applications. The design is mindful of practical constraints such the data center's limited visibility into hosted application performance. We evaluate the system using workloads derived from real world data center traces.
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paper_title: Benefits and limitations of tapping into stored energy for datacenters

paper_content:

Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this paper, we present a novel knob of energy buffer (eBuff) available in the form of UPS batteries in datacenters for this cost optimization. Intuitively, eBuff stores energy in UPS batteries during "valleys" - periods of lower demand, which can be drained during "peaks" - periods of higher demand. UPS batteries are normally used as a fail-over mechanism to transition to captive power sources upon utility failure. Furthermore, frequent discharges can cause UPS batteries to fail prematurely. We conduct detailed analysis of battery operation to figure out feasible operating regions given such battery lifetime and datacenter availability concerns. Using insights learned from this analysis, we develop peak reduction algorithms that combine the UPS battery knob with existing throttling based techniques for minimizing datacenter power costs. Using an experimental platform, we offer insights about Op-ex savings offered by eBuff for a wide range of workload peaks/valleys, UPS provisioning, and application SLA constraints. We find that eBuff can be used to realize 15-45% peak power reduction, corresponding to 6-18% savings in Op-ex across this spectrum. eBuff can also play a role in reducing Cap-ex costs by allowing tighter overbooking of power infrastructure components and we quantify the extent of such Cap-ex savings. To our knowledge, this is the first paper to exploit stored energy - typically lying untapped in the datacenter - to address the peak power draw problem.
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paper_title: Virtualizing power distribution in datacenters

paper_content:

Power infrastructure contributes to a significant portion of datacenter expenditures. Overbooking this infrastructure for a high percentile of the needs is becoming more attractive than for occasional peaks. There exist several computing knobs to cap the power draw within such under-provisioned capacity. Recently, batteries and other energy storage devices have been proposed to provide a complementary alternative to these knobs, which when decentralized (or hierarchically placed), can temporarily take the load to suppress power peaks propagating up the hierarchy. With aggressive under-provisioning, the power hierarchy becomes as central a datacenter resource as other computing resources, making it imperative to carefully allocate, isolate and manage this resource (including batteries), across applications. Towards this goal, we present vPower, a software system to virtualize power distribution. vPower includes mechanisms and policies to provide a virtual power hierarchy for each application. It leverages traditional computing knobs as well as batteries, to apportion and manage the infrastructure between co-existing applications in the hierarchy. vPower allows applications to specify their power needs, performs admission control and placement, dynamically monitors power usage, and enforces allocations for fairness and system efficiency. Using several datacenter applications, and a 2-level power hierarchy prototype containing batteries at both levels, we demonstrate the effectiveness of vPower when working in an under-provisioned power infrastructure, using the right computing knobs and the right batteries at the right time. Results show over 50% improved system utilization and scale-out for vPower's over-booking, and between 12-28% better application performance than traditional power-capping control knobs. It also ensures isolation between applications competing for power.
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paper_title: Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters

paper_content:

Datacenters spend $10-25 per watt in provisioning their power infrastructure, regardless of the watts actually consumed. Since peak power needs arise rarely, provisioning power infrastructure for them can be expensive. One can, thus, aggressively under-provision infrastructure assuming that simultaneous peak draw across all equipment will happen rarely. The resulting non-zero probability of emergency events where power needs exceed provisioned capacity, however small, mandates graceful reaction mechanisms to cap the power draw instead of leaving it to disruptive circuit breakers/fuses. Existing strategies for power capping use temporal knobs local to a server that throttle the rate of execution (using power modes), and/or spatial knobs that redirect/migrate excess load to regions of the datacenter with more power headroom. We show these mechanisms to have performance degrading ramifications, and propose an entirely orthogonal solution that leverages existing UPS batteries to temporarily augment the utility supply during emergencies. We build an experimental prototype to demonstrate such power capping on a cluster of 8 servers, each with an individual battery, and implement several online heuristics in the context of different datacenter workloads to evaluate their effectiveness in handling power emergencies. We show that: (i) our battery-based solution can handle emergencies of short duration on its own, (ii) supplement existing reaction mechanisms to enhance their efficacy for longer emergencies, and (iii) battery even provide feasible options when other knobs do not suffice.
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paper_title: Power routing: dynamic power provisioning in the data center

paper_content:

Data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. To squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. To ensure availability, these proposals employ power capping, which throttles server performance during utilization spikes to enforce safe power budgets. However, because budgets must be enforced locally -- at each power distribution unit (PDU) -- local utilization spikes may force throttling even when power delivery capacity is available elsewhere. Moreover, the need to maintain reserve capacity for fault tolerance on power delivery paths magnifies the impact of utilization spikes. ::: In this paper, we develop mechanisms to better utilize installed power infrastructure, reducing reserve capacity margins and avoiding performance throttling. Unlike conventional high-availability data centers, where collocated servers share identical primary and secondary power feeds, we reorganize power feeds to create shuffled power distribution topologies. Shuffled topologies spread secondary power feeds over numerous PDUs, reducing reserve capacity requirements to tolerate a single PDU failure. Second, we propose Power Routing, which schedules IT load dynamically across redundant power feeds to: (1) shift slack to servers with growing power demands, and (2) balance power draw across AC phases to reduce heating and improve electrical stability. We describe efficient heuristics for scheduling servers to PDUs (an NP-complete problem). Using data collected from nearly 1000 servers in three production facilities, we demonstrate that these mechanisms can reduce the required power infrastructure capacity relative to conventional high-availability data centers by 32% without performance degradation.
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paper_title: Agile, efficient virtualization power management with low-latency server power states

paper_content:

One of the main driving forces of the growing adoption of virtualization is its dramatic simplification of the provisioning and dynamic management of IT resources. By decoupling running entities from the underlying physical resources, and by providing easy-to-use controls to allocate, deallocate and migrate virtual machines (VMs) across physical boundaries, virtualization opens up new opportunities for improving overall system resource use and power efficiency. While a range of techniques for dynamic, distributed resource management of virtualized systems have been proposed and have seen their widespread adoption in enterprise systems, similar techniques for dynamic power management have seen limited acceptance. The main barrier to dynamic, power-aware virtualization management stems not from the limitations of virtualization, but rather from the underlying physical systems; and in particular, the high latency and energy cost of power state change actions suited for virtualization power management. In this work, we first explore the feasibility of low-latency power states for enterprise server systems and demonstrate, with real prototypes, their quantitative energy-performance trade offs compared to traditional server power states. Then, we demonstrate an end-to-end power-aware virtualization management solution leveraging these states, and evaluate the dramatically-favorable power-performance characteristics achievable with such systems. We present, via both real system implementations and scale-out simulations, that virtualization power management with low-latency server power states can achieve comparable overheads as base distributed resource management in virtualized systems, and thus can benefit from the same level of adoption, while delivering close to energy-proportional power efficiency.
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paper_title: PowerNap: eliminating server idle power

paper_content:

Data center power consumption is growing to unprecedented levels: the EPA estimates U.S. data centers will consume 100 billion kilowatt hours annually by 2011. Much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. Typical idle periods though frequent--last seconds or less, confounding simple energy-conservation approaches. In this paper, we propose PowerNap, an energy-conservation approach where the entire system transitions rapidly between a high-performance active state and a near-zero-power idle state in response to instantaneous load. Rather than requiring fine-grained power-performance states and complex load-proportional operation from each system component, PowerNap instead calls for minimizing idle power and transition time, which are simpler optimization goals. Based on the PowerNap concept, we develop requirements and outline mechanisms to eliminate idle power waste in enterprise blade servers. Because PowerNap operates in low-efficiency regions of current blade center power supplies, we introduce the Redundant Array for Inexpensive Load Sharing (RAILS), a power provisioning approach that provides high conversion efficiency across the entire range of PowerNap's power demands. Using utilization traces collected from enterprise-scale commercial deployments, we demonstrate that, together, PowerNap and RAILS reduce average server power consumption by 74%.
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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: Managing distributed ups energy for effective power capping in data centers

paper_content:

Power over-subscription can reduce costs for modern data centers. However, designing the power infrastructure for a lower operating power point than the aggregated peak power of all servers requires dynamic techniques to avoid high peak power costs and, even worse, tripping circuit breakers. This work presents an architecture for distributed per-server UPSs that stores energy during low activity periods and uses this energy during power spikes. This work leverages the distributed nature of the UPS batteries and develops policies that prolong the duration of their usage. The specific approach shaves 19.4% of the peak power for modern servers, at no cost in performance, allowing the installation of 24% more servers within the same power budget. More servers amortize infrastructure costs better and, hence, reduce total cost of ownership per server by 6.3%.
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paper_title: Greening geographical load balancing

paper_content:

Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.
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paper_title: It's not easy being green

paper_content:

Large-scale Internet applications, such as content distribution networks, are deployed across multiple datacenters and consume massive amounts of electricity. To provide uniformly low access latencies, these datacenters are geographically distributed and the deployment size at each location reflects the regional demand for the application. Consequently, an application's environmental impact can vary significantly depending on the geographical distribution of end-users, as electricity cost and carbon footprint per watt is location specific. In this paper, we describe FORTE: Flow Optimization based framework for request-Routing and Traffic Engineering. FORTE dynamically controls the fraction of user traffic directed to each datacenter in response to changes in both request workload and carbon footprint. It allows an operator to navigate the three-way tradeoff between access latency, carbon footprint, and electricity costs and to determine an optimal datacenter upgrade plan in response to increases in traffic load. We use FORTE to show that carbon taxes or credits are impractical in incentivizing carbon output reduction by providers of large-scale Internet applications. However, they can reduce carbon emissions by 10% without increasing the mean latency nor the electricity bill.
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paper_title: Blink: managing server clusters on intermittent power

paper_content:

Reducing the energy footprint of data centers continues to receive significant attention due to both its financial and environmental impact. There are numerous methods that limit the impact of both factors, such as expanding the use of renewable energy or participating in automated demand-response programs. To take advantage of these methods, servers and applications must gracefully handle intermittent constraints in their power supply. In this paper, we propose blinking---metered transitions between a high-power active state and a low-power inactive state---as the primary abstraction for conforming to intermittent power constraints. We design Blink, an application-independent hardware-software platform for developing and evaluating blinking applications, and define multiple types of blinking policies. We then use Blink to design BlinkCache, a blinking version of memcached, to demonstrate the effect of blinking on an example application. Our results show that a load-proportional blinking policy combines the advantages of both activation and synchronous blinking for realistic Zipf-like popularity distributions and wind/solar power signals by achieving near optimal hit rates (within 15% of an activation policy), while also providing fairer access to the cache (within 2% of a syn- chronous policy) for equally popular objects.
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paper_title: The answer is blowing in the wind: Analysis of powering Internet data centers with wind energy

paper_content:

Internet-scale data centers (IDCs) have rapidly proliferated to such an extent that their energy consumption and GreenHouse Gas (GHG) emissions have become an important concern to society. As a result, many IDC operators have started using renewable energy, e.g., wind power, to power their data centers. Unfortunately, the utilization of wind energy has stayed at a low ratio due to the intermittent nature of wind. This paper makes the case that it is in fact possible for a distributed IDC system to exploit multiple uncorrelated wind energy sources to significantly reduce the effect of intermittency and nearly achieve “entirely green” cloud-scale services. This result is obtained based on the analysis of real-world wind power traces from 69 wind farms. The idea is to leverage the front-end load dispatching server to send work to the location where wind power is available. We propose a wind-power-aware (WPA) policy that routes jobs based only on the current states of workloads and wind power availabilities in the data centers. We show that with the WPA policy more than 95% of energy consumption in IDCs can in fact be satisfied by wind power, and, secondly, that achieving this does not require the delaying of processing of jobs due to wind availability. We also show that the locations where data centers are placed play an important role in achieving high wind power utilization. Our analysis shows that wind power utilization can generally lie in a range from 44% to 96%, depending on how the locations of wind farms are selected. We propose a method for location selection that uses the coefficient of variation instead of the correlation coefficient, and show that with this method the utilization can lie in the high end of the above range. Finally, we verify these results by simulations that are based on real-world traces for both workloads and wind power generations.
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paper_title: Greenware: Greening cloud-scale data centers to maximize the use of renewable energy

paper_content:

To reduce the negative environmental implications (e.g., CO 2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets. ::: ::: In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with real-world weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.
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paper_title: iSwitch: coordinating and optimizing renewable energy powered server clusters

paper_content:

Large-scale computing systems such as data centers are facing increasing pressure to cap their carbon footprint. Integrating emerging clean energy solutions into computer system design therefore gains great significance in the green computing era. While some pioneering work on tracking variable power budget show promising energy efficiency, they are not suitable for data centers due to lack of performance guarantee when renewable generation is low and fluctuant. In addition, our characterization of wind power behavior reveals that data centers designed to track the intermittent renewable power incur up to 4X performance loss due to inefficient and redundant load matching activities. As a result, mitigating operational overhead while still maintaining desired energy utilization becomes the most significant challenge in managing server clusters on intermittent renewable energy generation. In this paper we take a first step in digging into the operational overhead of renewable energy powered data center. We propose iSwitch, a lightweight server power management that follows renewable power variation characteristics, leverages existing system infrastructures, and applies supply/load cooperative scheme to mitigate the performance overhead. Comparing with state-of-the-art renewable energy driven system design, iSwitch could mitigate average network traffic by 75%, peak network traffic by 95%, and reduce 80% job waiting time while still maintaining 96% renewable energy utilization. We expect that our work can help computer architects make informed decisions on sustainable and high-performance system design.
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paper_title: Free Lunch: Exploiting Renewable Energy for Computing

paper_content:

This paper argues for "Free Lunch", a computation architecture that exploits otherwise wasted renewable energy by (i) colocating datacentres with these remote energy sources, (ii) connecting them over a dedicated network, and (iii) providing a software framework that supports the seamless execution and migration of virtual machines in the platform according to power availability. This work motivates and outlines the architecture and demonstrates its viability with a case study. Additionally, we discuss the major technical challenges facing the successful deployment of Free Lunch and the limiting factors inherent in its design.
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paper_title: GreenHadoop: leveraging green energy in data-processing frameworks

paper_content:

Interest has been growing in powering datacenters (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) energy drawn from the electrical grid, they are not always available. This means that energy demand and supply must be matched, if we are to take full advantage of the green energy to minimize brown energy consumption. In this paper, we investigate how to manage a datacenter's computational workload to match the green energy supply. In particular, we consider data-processing frameworks, in which many background computations can be delayed by a bounded amount of time. We propose GreenHadoop, a MapReduce framework for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenHadoop predicts the amount of solar energy that will be available in the near future, and schedules the MapReduce jobs to maximize the green energy consumption within the jobs' time bounds. If brown energy must be used to avoid time bound violations, GreenHadoop selects times when brown energy is cheap, while also managing the cost of peak brown power consumption. Our experimental results demonstrate that GreenHadoop can significantly increase green energy consumption and decrease electricity cost, compared to Hadoop.
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paper_title: GreenSlot: Scheduling energy consumption in green datacenters

paper_content:

In this paper, we propose GreenSlot, a parallel batch job scheduler for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meeting the jobs' deadlines. If grid energy must be used to avoid deadline violations, the scheduler selects times when it is cheap. Our results for production scientific workloads demonstrate that Green-Slot can increase green energy consumption by up to 117% and decrease energy cost by up to 39%, compared to a conventional scheduler. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem.
---
paper_title: Greening geographical load balancing

paper_content:

Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.
---
paper_title: CloudNaaS: a cloud networking platform for enterprise applications

paper_content:

Enterprises today face several challenges when hosting line-of-business applications in the cloud. Central to many of these challenges is the limited support for control over cloud network functions, such as, the ability to ensure security, performance guarantees or isolation, and to flexibly interpose middleboxes in application deployments. In this paper, we present the design and implementation of a novel cloud networking system called CloudNaaS. Customers can leverage CloudNaaS to deploy applications augmented with a rich and extensible set of network functions such as virtual network isolation, custom addressing, service differentiation, and flexible interposition of various middleboxes. CloudNaaS primitives are directly implemented within the cloud infrastructure itself using high-speed programmable network elements, making CloudNaaS highly efficient. We evaluate an OpenFlow-based prototype of CloudNaaS and find that it can be used to instantiate a variety of network functions in the cloud, and that its performance is robust even in the face of large numbers of provisioned services and link/device failures.
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paper_title: Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers

paper_content:

Ensuring the quality of service (QoS) for latency-sensitive applications while allowing co-locations of multiple applications on servers is critical for improving server utilization and reducing cost in modern warehouse-scale computers (WSCs). Recent work relies on static profiling to precisely predict the QoS degradation that results from performance interference among co-running applications to increase the number of "safe" co-locations. However, these static profiling techniques have several critical limitations: 1) a priori knowledge of all workloads is required for profiling, 2) it is difficult for the prediction to capture or adapt to phase or load changes of applications, and 3) the prediction technique is limited to only two co-running applications. To address all of these limitations, we present Bubble-Flux, an integrated dynamic interference measurement and online QoS management mechanism to provide accurate QoS control and maximize server utilization. Bubble-Flux uses a Dynamic Bubble to probe servers in real time to measure the instantaneous pressure on the shared hardware resources and precisely predict how the QoS of a latency-sensitive job will be affected by potential co-runners. Once "safe" batch jobs are selected and mapped to a server, Bubble-Flux uses an Online Flux Engine to continuously monitor the QoS of the latency-sensitive application and control the execution of batch jobs to adapt to dynamic input, phase, and load changes to deliver satisfactory QoS. Batch applications remain in a state of flux throughout execution. Our results show that the utilization improvement achieved by Bubble-Flux is up to 2.2x better than the prior static approach.
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paper_title: Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations

paper_content:

As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem. In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.
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paper_title: Temperature-aware microarchitecture: Modeling and implementation

paper_content:

With cooling costs rising exponentially, designing cooling solutions for worst-case power dissipation is prohibitively expensive. Chips that can autonomously modify their execution and power-dissipation characteristics permit the use of lower-cost cooling solutions while still guaranteeing safe temperature regulation. Evaluating techniques for this dynamic thermal management (DTM), however, requires a thermal model that is practical for architectural studies.This paper describes HotSpot, an accurate yet fast and practical model based on an equivalent circuit of thermal resistances and capacitances that correspond to microarchitecture blocks and essential aspects of the thermal package. Validation was performed using finite-element simulation. The paper also introduces several effective methods for DTM: "temperature-tracking" frequency scaling, "migrating computation" to spare hardware units, and a "hybrid" policy that combines fetch gating with dynamic voltage scaling. The latter two achieve their performance advantage by exploiting instruction-level parallelism, showing the importance of microarchitecture research in helping control the growth of cooling costs.Modeling temperature at the microarchitecture level also shows that power metrics are poor predictors of temperature, that sensor imprecision has a substantial impact on the performance of DTM, and that the inclusion of lateral resistances for thermal diffusion is important for accuracy.
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paper_title: Temperature Aware Workload Management in Geo-distributed Datacenters.

paper_content:

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: Mercury and freon: temperature emulation and management for server systems

paper_content:

Power densities have been increasing rapidly at all levels of server systems. To counter the high temperatures resulting from these densities, systems researchers have recently started work on softwarebased thermal management. Unfortunately, research in this new area has been hindered by the limitations imposed by simulators and real measurements. In this paper, we introduce Mercury, a software suite that avoids these limitations by accurately emulating temperatures based on simple layout, hardware, and componentutilization data. Most importantly, Mercury runs the entire software stack natively, enables repeatable experiments, and allows the study of thermal emergencies without harming hardware reliability. We validate Mercury using real measurements and a widely used commercial simulator. We use Mercury to develop Freon, a system that manages thermal emergencies in a server cluster without unnecessary performance degradation. Mercury will soon become available from http://www.darklab.rutgers.edu.
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paper_title: Smart Cooling of Data Centers

paper_content:

A cooling system is configured to adjust cooling fluid flow to various racks located throughout a data center based upon the detected or anticipated temperatures at various locations throughout the data center. In one respect, by substantially increasing the cooling fluid flow to those racks dissipating greater amounts of heat and by substantially decreasing the cooling fluid flow to those racks dissipating lesser amounts of heat, the amount of energy required to operate the cooling system may be relatively reduced. Thus, instead of operating the devices, e.g., compressors, fans, etc., of the cooling system at substantially 100 percent of the anticipated heat dissipation from the racks, those devices may be operated according to the actual cooling needs. In addition, the racks may be positioned throughout the data center according to their anticipated heat loads to thereby enable computer room air conditioning (CRAC) units located at various positions throughout the data center to operate in a more efficient manner. In one respect, the positioning of the racks may be determined through implementation of numerical modeling and metrology of the cooling fluid flow throughout the data center. In addition, the numerical modeling may be implemented to control the volume flow rate and velocity of the cooling fluid flow through each of the vents.
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paper_title: Exploiting thermal energy storage to reduce data center capital and operating expenses

paper_content:

Power shaving has recently been proposed to dynamically shave the power peaks of a data center with energy storage devices (ESD), such that more servers can be safely hosted. In addition to the reduction of capital investment (cap-ex), power shaving also helps cut the electricity bills (op-ex) of a data center by reducing the high utility tariffs related to peak power. However, existing work on power shaving focuses exclusively on electrical ESDs (e.g., UPS batteries) to shave the server-side power demand. In this paper, we propose TE-Shave, a generalized power shaving framework that exploits both UPS batteries and a new knob, thermal energy storage (TES) tanks equipped in many data centers. Specifically, TE-Shave utilizes stored cold water or ice to manipulate the cooling power, which accounts for 30–40% of the total power cost of a data center. Our extensive evaluation with real-world workload traces shows that TE-Shave saves cap-ex and op-ex up to $2,668/day and $825/day, respectively, for a data center with 17,920 servers. Even for future data centers that are projected to have more efficient cooling and thus a smaller portion of cooling power, e.g., a quarter of today's level, TE-Shave still leads to 28% more savings than existing work that focuses only on the server-side power. TE-Shave is also coordinated with traditional TES solutions for further reduced op-ex.
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paper_title: T*: A data-centric cooling energy costs reduction approach for Big Data analytics cloud

paper_content:

Explosion in Big Data has led to a surge in extremely large-scale Big Data analytics platforms, resulting in burgeoning energy costs. Big Data compute model mandates strong data-locality for computational performance, and moves computations to data. State-of-the-art cooling energy management techniques rely on thermal-aware computational job placement/migration and are inherently data-placement-agnostic in nature. T* takes a novel, data-centric approach to reduce cooling energy costs and to ensure thermal-reliability of the servers. T* is cognizant of the uneven thermal-profile and differences in thermal-reliability-driven load thresholds of the servers, and the differences in the computational jobs arrival rate, size, and evolution life spans of the Big Data placed in the cluster. Based on this knowledge, and coupled with its predictive file models and insights, T* does proactive, thermal-aware file placement, which implicitly results in thermal-aware job placement in the Big Data analytics compute model. Evaluation results with one-month long real-world Big Data analytics production traces from Yahoo! show up to 42% reduction in the cooling energy costs with T* courtesy of its lower and more uniform thermal-profile and 9x better performance than the state-of-the-art data-agnostic cooling techniques.
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paper_title: Balance of power: dynamic thermal management for Internet data centers

paper_content:

Internet-based applications and their resulting multitier distributed architectures have changed the focus of design for large-scale Internet computing. Internet server applications execute in a horizontally scalable topology across hundreds or thousands of commodity servers in Internet data centers. Increasing scale and power density significantly impacts the data center's thermal properties. Effective thermal management is essential to the robustness of mission-critical applications. Internet service architectures can address multisystem resource management as well as thermal management within data centers.
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paper_title: Making Scheduling “ Cool ” : Temperature-Aware Workload Placement in Data Centers ∗

paper_content:

Trends towards consolidation and higher-density computing configurations make the problem of heat management one of the critical challenges in emerging data centers. Conventional approaches to addressing this problem have focused at the facilities level to develop new cooling technologies or optimize the delivery of cooling. In contrast to these approaches, our paper explores an alternate dimension to address this problem, namely a systems-level solution to control the heat generation through temperature-aware workload placement. ::: ::: We first examine a theoretic thermodynamic formulation that uses information about steady state hot spots and cold spots in the data center and develop real-world scheduling algorithms. Based on the insights from these results, we develop an alternate approach. Our new approach leverages the non-intuitive observation that the source of cooling inefficiencies can often be in locations spatially uncorrelated with its manifested consequences; this enables additional energy savings. Overall, our results demonstrate up to a factor of two reduction in annual data center cooling costs over location-agnostic workload distribution, purely through software optimizations without the need for any costly capital investment.
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paper_title: Temperature management in data centers: why some (might) like it hot

paper_content:

The energy consumed by data centers is starting to make up a significant fraction of the world's energy consumption and carbon emissions. A large fraction of the consumed energy is spent on data center cooling, which has motivated a large body of work on temperature management in data centers. Interestingly, a key aspect of temperature management has not been well understood: controlling the setpoint temperature at which to run a data center's cooling system. Most data centers set their thermostat based on (conservative) suggestions by manufacturers, as there is limited understanding of how higher temperatures will affect the system. At the same time, studies suggest that increasing the temperature setpoint by just one degree could save 2-5% of the energy consumption. This paper provides a multi-faceted study of temperature management in data centers. We use a large collection of field data from different production environments to study the impact of temperature on hardware reliability, including the reliability of the storage subsystem, the memory subsystem and server reliability as a whole. We also use an experimental testbed based on a thermal chamber and a large array of benchmarks to study two other potential issues with higher data center temperatures: the effect on server performance and power. Based on our findings, we make recommendations for temperature management in data centers, that create the potential for saving energy, while limiting negative effects on system reliability and performance.
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paper_title: Fighting fire with fire: modeling the datacenter-scale effects of targeted superlattice thermal management

paper_content:

Local thermal hot-spots in microprocessors lead to worst-case provisioning of global cooling resources, especially in large-scale systems where cooling power can be 50~100% of IT power. Further, the efficiency of cooling solutions degrade non-linearly with supply temperature. Recent advances in active cooling techniques have shown on-chip thermoelectric coolers (TECs) to be very efficient at selectively eliminating small hot-spots. Applying current to a superlattice TEC-film that is deposited between silicon and the heat spreader results in a Peltier effect, which spreads the heat and lowers the temperature of the hot-spot significantly and improves chip reliability. In this paper, we propose that hot-spot mitigation using thermoelectric coolers can be used as a power management mechanism to allow global coolers to be provisioned for a better worst case temperature leading to substantial savings in cooling power. In order to quantify the potential power savings from using TECs in data center servers, we present a detailed power model that integrates on-chip dynamic and leakage power sour-ces, heat diffusion through the entire chip, TEC and global cooler efficiencies, and all their mutual interactions. Our multi-scale analysis shows that, for a typical data center, TECs allow global coolers to operate at higher temperatures without degrading chip lifetime, and thus save ~27% cooling power on average while providing the same processor reliability as a data center running at 288K.
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paper_title: Temperature Aware Workload Management in Geo-distributed Datacenters.

paper_content:

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.
---
paper_title: Optimization and control of cooling microgrids for data centers

paper_content:

Reliable operation of today's data centers requires a tremendous amount of electricity to power both the IT equipment and the supporting cooling facilities. As much as half of total data center electricity consumption can be attributed to the cooling systems required to maintain the thermal status of IT equipment. In order to lower the electricity usage of the cooling system and hence reduce the data center environmental footprint, alternative cooling resources, such as water and air-side economizers, are being exploited to supplement or replace the traditional chilled water based cooling schemes. The various cooling resource options, together with the mechanisms to distribute and deliver the cooling resource to IT equipment racks, constitute a cooling microgrid. In this paper, we present a holistic perspective for the optimization and control of the data center cooling microgrid. The holistic approach optimizes the sourcing and distribution of cooling resources from the site portfolio in response to real-time weather changes and site demand. The cooling microgrid optimization and control framework has been implemented in a research data center; we estimate that the framework cuts the yearly cooling costs by 30%.
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paper_title: Server Heat Load Based CRAC Fan Controller Paired With Rear Door Heat Exchanger

paper_content:

This paper discusses the preliminary design of a controller for the cooling air conditioning units (CRAC) fan speed based on server loads. The CRAC is paired with a rear door heat exchanger. The main purpose in implementing this controller is the reduce energy consumption and to have finer control in the provision of cooling within a data center. Tools that will be essential for developing this controller are CRACs with variable frequency drives (VFD), BCMS power monitoring, data archiving software (PI from OSISOFT) and MATLAB. For this experiment, the zone of interest in the data center consists of 10 racks of IBM Blade Centers with a maximum electrical load of approximately 24 kW each and a total of 3360 nodes. Results are compared with the conventional cooling method to quantify energy savings and corresponding chiller coefficient of performance (COP).Copyright © 2011 by ASME
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paper_title: Self-benchmarking Guide for Data Centers: Metrics, Benchmarks, Actions

paper_content:

Self-benchmarking Guide for Data Centers: Metrics, Benchmarks, Actions Paul Mathew, Ph.D. Srirupa Ganguly Steve Greenberg, P.E. Dale Sartor, P.E. Lawrence Berkeley National Laboratory Berkeley, California Prepared for: New York State Energy Research and Development Authority 13 July 2009
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paper_title: Implementing the data center energy productivity metric

paper_content:

As data centers proliferate in size and number, the endeavor to improve their energy efficiency and productivity is becoming increasingly important. We discuss the properties of a number of the proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented, high-performance computing data center. We found that DCeP was successful in clearly distinguishing different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve (or even maximize) energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and among data centers.
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paper_title: PUE: The Green Grid metric for evaluating the energy efficiency in DC (Data Center). Measurement method using the power demand

paper_content:

PUE (Power Usage Effectiveness) is a metric developed by the Green Grid widely accepted for measuring energy efficiency in Data Centers (DC). In summary, this metric relates all the energy consumed by the DC by that consumed by the servers. “Where to measure” will depend on the particular characteristics of each facility. “What” and “how” to measure is the goal of this paper. In its most simplified form, PUE is the ratio between annual energy consumed by the DC and annual energy consumed by the servers. There are recommendations as to take measurements in shorter time periods (monthly, weekly, daily, etc‥), whose results may show the evolution of efficiency with time and give greater understanding of the behavior of the DC to take corrective actions. In this work a measurement method will be treated based on the record of the power demand integrated in 15 min or 1 h periods, due to the ease of using standard measuring equipment to obtain it, the reliability and accuracy of data and usefulness of their results.
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paper_title: Self-benchmarking Guide for Data Centers: Metrics, Benchmarks, Actions

paper_content:

Self-benchmarking Guide for Data Centers: Metrics, Benchmarks, Actions Paul Mathew, Ph.D. Srirupa Ganguly Steve Greenberg, P.E. Dale Sartor, P.E. Lawrence Berkeley National Laboratory Berkeley, California Prepared for: New York State Energy Research and Development Authority 13 July 2009
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paper_title: PowerTracer: tracing requests in multi-tier services to diagnose energy inefficiency

paper_content:

As energy has become one of the key operating costs in running a data center and power waste commonly exists, it is essential to observe and reduce energy inefficiency inside data centers. In this paper, we develop an innovative framework, called PowerTracer, for diagnosing energy-inefficiency. Inside the framework, we first present a resource tracing method based on request tracing in multi-tier services of black boxes. Then, we propose a generalized methodology of applying a request tracing approach for energy-inefficiency diagnosis in multi-tier service systems. With insights into service performance and resource consumption of individual requests, we develop a bottleneck diagnosis tool that pinpoints the root causes of energy inefficiency. We implement the prototype and conduct experiments to validate its effectiveness.
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paper_title: GreenCloud: a packet-level simulator of energy-aware cloud computing data centers

paper_content:

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs.
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paper_title: SimWare: A Holistic Warehouse-Scale Computer Simulator

paper_content:

To optimize datacenter energy efficiency, SimWare analyzes the power consumption of servers, cooling units, and fans as well as the effects of heat recirculation and air supply timing. Experiments using SimWare show a high loss of cooling efficiency due to the nonuniform inlet air temperature distribution across servers.
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paper_title: GreenCloud: a new architecture for green data center

paper_content:

Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We present the GreenCloud architecture, which aims to reduce data center power consumption, while guarantee the performance from users' perspective. GreenCloud architecture enables comprehensive online-monitoring, live virtual machine migration, and VM placement optimization. To verify the efficiency and effectiveness of the proposed architecture, we take an online real-time game, Tremulous, as a VM application. Evaluation results show that we can save up to 27% of the energy when applying GreenCloud architecture.
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paper_title: GDCSim: A tool for analyzing Green Data Center design and resource management techniques

paper_content:

Energy consumption in data centers can be reduced by efficient design of the data centers and efficient management of computing resources and cooling units. A major obstacle in the analysis of data centers is the lack of a holistic simulator, where the impact of new computing resource (or cooling) management techniques can be tested with diffierent designs (i.e., layouts and configurations) of data centers. To fill this gap, this paper proposes Green Data Center Simulator (GDCSim) for studying the energy efficiency of data centers under various data center geometries, workload characteristics, platform power management schemes, and scheduling algorithms. GDCSim is used to iteratively design green data centers. Further, it is validated against established CFD simulators. GDCSim is developed as a part of the BlueTool infrastructure project at Impact Lab.
---
paper_title: PowerTracer: tracing requests in multi-tier services to diagnose energy inefficiency

paper_content:

As energy has become one of the key operating costs in running a data center and power waste commonly exists, it is essential to observe and reduce energy inefficiency inside data centers. In this paper, we develop an innovative framework, called PowerTracer, for diagnosing energy-inefficiency. Inside the framework, we first present a resource tracing method based on request tracing in multi-tier services of black boxes. Then, we propose a generalized methodology of applying a request tracing approach for energy-inefficiency diagnosis in multi-tier service systems. With insights into service performance and resource consumption of individual requests, we develop a bottleneck diagnosis tool that pinpoints the root causes of energy inefficiency. We implement the prototype and conduct experiments to validate its effectiveness.
---
paper_title: GreenCloud: a packet-level simulator of energy-aware cloud computing data centers

paper_content:

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs.
---
paper_title: Cooperative Monitoring for Internet Data Centers

paper_content:

Recent outages in several Web services have demonstrated the potential damage of availability disruptions, since millions of end users rely on these services powered by thousands of servers in large-scale Internet data centers. As Internet data centers continue to grow in scale and complexity, it has become a daunting task to monitor and manage thousands of servers simultaneously. This paper presents a cooperative monitoring framework to continuously monitor availability of thousands of servers in Internet data centers. We propose a simple yet effective algorithm for locating monitor nodes for the purposes of load balancing and resilience, and demonstrate the performance of this method through simulations based on dataset collected from a large Internet content provider. The results show that the monitoring load are well divided among the servers in the data centers, and cooperative monitoring adapts gracefully to the events of monitoring node failures.
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paper_title: GreenCloud: a new architecture for green data center

paper_content:

Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We present the GreenCloud architecture, which aims to reduce data center power consumption, while guarantee the performance from users' perspective. GreenCloud architecture enables comprehensive online-monitoring, live virtual machine migration, and VM placement optimization. To verify the efficiency and effectiveness of the proposed architecture, we take an online real-time game, Tremulous, as a VM application. Evaluation results show that we can save up to 27% of the energy when applying GreenCloud architecture.
---
paper_title: GDCSim: A tool for analyzing Green Data Center design and resource management techniques

paper_content:

Energy consumption in data centers can be reduced by efficient design of the data centers and efficient management of computing resources and cooling units. A major obstacle in the analysis of data centers is the lack of a holistic simulator, where the impact of new computing resource (or cooling) management techniques can be tested with diffierent designs (i.e., layouts and configurations) of data centers. To fill this gap, this paper proposes Green Data Center Simulator (GDCSim) for studying the energy efficiency of data centers under various data center geometries, workload characteristics, platform power management schemes, and scheduling algorithms. GDCSim is used to iteratively design green data centers. Further, it is validated against established CFD simulators. GDCSim is developed as a part of the BlueTool infrastructure project at Impact Lab.
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Title: Green Data Centers: A Survey, Perspectives, and Future Directions

Section 1: Introduction
Description 1: Introduce the importance of data centers in modern IT infrastructure, their energy consumption issues, and the need for making data centers "green."

Section 2: Energy Efficiency
Description 2: Discuss methods for curbing energy use in data centers with energy efficiency techniques such as dynamic speed scaling and power-down mechanisms.

Section 3: Data Center Resource Management
Description 3: Present research on using virtualization and cloud computing to improve resource utilization and reduce energy consumption in data centers.

Section 4: Temperature and Thermal Control
Description 4: Cover strategies for managing temperature and thermal control in data centers to reduce cooling costs and improve energy efficiency.

Section 5: Green Metrics, Monitoring, and Experimental Techniques
Description 5: Introduce metrics to quantify the greenness of data centers, and discuss monitoring and experimental methods to optimize energy consumption and reduce carbon emissions.

Section 6: Conclusion
Description 6: Summarize the current research on green data centers, discuss key insights and contributions, and highlight future research challenges and directions.