A survey on energy-efficient data management
---
paper_title: Exploring power-performance tradeoffs in database systems

paper_content:

With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11% - 22% can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements.1
---
paper_title: Models and Metrics to Enable Energy-Efficiency Optimizations

paper_content:

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.
---
paper_title: Analyzing the energy efficiency of a database server

paper_content:

Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization. We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.
---
paper_title: Toward energy-efficient computing

paper_content:

What will it take to make server-side computing more energy efficient?
---
paper_title: The Case for Energy-Proportional Computing

paper_content:

Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems.
---
paper_title: Energy-Efficient Scheduling for Real-Time Systems on Dynamic Voltage Scaling (DVS) Platforms

paper_content:

Energy-efficient designs have played import roles for hardware and software implementations for a decade. With the advanced technology of VLSI circuit designs, energy-efficiency can be achieved by adopting the dynamic voltage scaling (DVS) technique. In this paper, we survey the studies for energy-efficient scheduling in real-time systems on DVS platforms to cover both theoretical and practical issues.
---
paper_title: Analyzing the energy efficiency of a database server

paper_content:

Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization. We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.
---
paper_title: Toward energy-efficient computing

paper_content:

What will it take to make server-side computing more energy efficient?
---
paper_title: Towards Eco-friendly Database Management Systems

paper_content:

Database management systems (DBMSs) have largely ignored the task of managing the energy consumed during query processing. Both economical and environmental factors now require that DBMSs pay close attention to energy consumption. In this paper we approach this issue by considering energy consumption as a first-class performance goal for query processing in a DBMS. We present two concrete techniques that can be used by a DBMS to directly manage the energy consumption. Both techniques trade energy consumption for performance. The first technique, called PVC, leverages the ability of modern processors to execute at lower processor voltage and frequency. The second technique, called QED, uses query aggregation to leverage common components of queries in a workload. Using experiments run on a commercial DBMS and MySQL, we show that PVC can reduce the processor energy consumption by 49% of the original consumption while increasing the response time by only 3%. On MySQL, PVC can reduce energy consumption by 20% with a response time penalty of only 6%. For simple selection queries with no predicate overlap, we show that QED can be used to gracefully trade response time for energy, reducing energy consumption by 54% for a 43% increase in average response time. In this paper we also highlight some research issues in the emerging area of energy-efficient data processing.
---
paper_title: Analyzing the energy efficiency of a database server

paper_content:

Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization. We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.
---
paper_title: Exploring power-performance tradeoffs in database systems

paper_content:

With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11% - 22% can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements.1
---
paper_title: The Claremont report on database research

paper_content:

In late May, 2008, a group of database researchers, architects, users and pundits met at the Claremont Resort in Berkeley, California to discuss the state of the research field and its impacts on practice. This was the seventh meeting of this sort in twenty years, and was distinguished by a broad consensus that we are at a turning point in the history of the field, due both to an explosion of data and usage scenarios, and to major shifts in computing hardware and platforms. Given these forces, we are at a time of opportunity for research impact, with an unusually large potential for influential results across computing, the sciences and society. This report details that discussion, and highlights the group's consensus view of new focus areas, including new database engine architectures, declarative programming languages, the interplay of structured and unstructured data, cloud data services, and mobile and virtual worlds. We also report on discussions of the community's growth, including suggestions for changes in community processes to move the research agenda forward, and to enhance impact on a broader audience.
---
paper_title: Energy Efficiency: The New Holy Grail of Data Management Systems Research

paper_content:

Energy costs are quickly rising in large-scale data centers and are soon projected to overtake the cost of hardware. As a result, data center operators have recently started turning into using more energy-friendly hardware. Despite the growing body of research in power management techniques, there has been little work to date on energy efficiency from a data management software perspective. In this paper, we argue that hardware-only approaches are only part of the solution, and that data management software will be key in optimizing for energy efficiency. We discuss the problems arising from growing energy use in data centers and the trends that point to an increasing set of opportunities for software-level optimizations. Using two simple experiments, we illustrate the potential of such optimizations, and, motivated by these examples, we discuss general approaches for reducing energy waste. Lastly, we point out existing places within database systems that are promising for energy-efficiency optimizations and urge the data management systems community to shift focus from performance-oriented research to energy-efficient computing.
---
paper_title: Building a power-aware database management system

paper_content:

In today's large-scale data centers, energy costs (i.e., the electricity bill) are projected to outgrow that of hardware. Despite a long history of research in energy-saving techniques, especially low-power hardware, little work has been done to improve the power efficiency of data management software. Power-aware computing research at the application level has been found to be synergistic to that at the hardware and OS levels because it can provide more opportunities for energy reduction in the underlying systems. This paper describes the author's thesis work on creating a power-aware database management (P-DBMS) and initial ideas on the design of such systems, with the focus on a power-aware query optimization module inside the DBMS. We discuss the main technical challenges in designing the optimizer and present our strategies to meet such challenges. We focus our discussions on a power model to accurately measure the energy costs of query executions plans, and a cost evaluation model for plan selection. An important feature of this work is the formal control-theoretic methods we use to model and optimize the database towards the performance and energy saving goals. This rigorous design methodology is in sharp contrast to heuristic-based adaptive solutions that rely on extensive empirical evaluation and manual tuning. Our experiments using a power-aware query optimizer under our initial design show that there exist significant potential in power/energy savings.
---
paper_title: Towards Eco-friendly Database Management Systems

paper_content:

Database management systems (DBMSs) have largely ignored the task of managing the energy consumed during query processing. Both economical and environmental factors now require that DBMSs pay close attention to energy consumption. In this paper we approach this issue by considering energy consumption as a first-class performance goal for query processing in a DBMS. We present two concrete techniques that can be used by a DBMS to directly manage the energy consumption. Both techniques trade energy consumption for performance. The first technique, called PVC, leverages the ability of modern processors to execute at lower processor voltage and frequency. The second technique, called QED, uses query aggregation to leverage common components of queries in a workload. Using experiments run on a commercial DBMS and MySQL, we show that PVC can reduce the processor energy consumption by 49% of the original consumption while increasing the response time by only 3%. On MySQL, PVC can reduce energy consumption by 20% with a response time penalty of only 6%. For simple selection queries with no predicate overlap, we show that QED can be used to gracefully trade response time for energy, reducing energy consumption by 54% for a 43% increase in average response time. In this paper we also highlight some research issues in the emerging area of energy-efficient data processing.
---
paper_title: Towards Eco-friendly Database Management Systems

paper_content:

Database management systems (DBMSs) have largely ignored the task of managing the energy consumed during query processing. Both economical and environmental factors now require that DBMSs pay close attention to energy consumption. In this paper we approach this issue by considering energy consumption as a first-class performance goal for query processing in a DBMS. We present two concrete techniques that can be used by a DBMS to directly manage the energy consumption. Both techniques trade energy consumption for performance. The first technique, called PVC, leverages the ability of modern processors to execute at lower processor voltage and frequency. The second technique, called QED, uses query aggregation to leverage common components of queries in a workload. Using experiments run on a commercial DBMS and MySQL, we show that PVC can reduce the processor energy consumption by 49% of the original consumption while increasing the response time by only 3%. On MySQL, PVC can reduce energy consumption by 20% with a response time penalty of only 6%. For simple selection queries with no predicate overlap, we show that QED can be used to gracefully trade response time for energy, reducing energy consumption by 54% for a 43% increase in average response time. In this paper we also highlight some research issues in the emerging area of energy-efficient data processing.
---
paper_title: Exploring power-performance tradeoffs in database systems

paper_content:

With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11% - 22% can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements.1
---
paper_title: Energy Efficiency: The New Holy Grail of Data Management Systems Research

paper_content:

Energy costs are quickly rising in large-scale data centers and are soon projected to overtake the cost of hardware. As a result, data center operators have recently started turning into using more energy-friendly hardware. Despite the growing body of research in power management techniques, there has been little work to date on energy efficiency from a data management software perspective. In this paper, we argue that hardware-only approaches are only part of the solution, and that data management software will be key in optimizing for energy efficiency. We discuss the problems arising from growing energy use in data centers and the trends that point to an increasing set of opportunities for software-level optimizations. Using two simple experiments, we illustrate the potential of such optimizations, and, motivated by these examples, we discuss general approaches for reducing energy waste. Lastly, we point out existing places within database systems that are promising for energy-efficiency optimizations and urge the data management systems community to shift focus from performance-oriented research to energy-efficient computing.
---
paper_title: Building a power-aware database management system

paper_content:

In today's large-scale data centers, energy costs (i.e., the electricity bill) are projected to outgrow that of hardware. Despite a long history of research in energy-saving techniques, especially low-power hardware, little work has been done to improve the power efficiency of data management software. Power-aware computing research at the application level has been found to be synergistic to that at the hardware and OS levels because it can provide more opportunities for energy reduction in the underlying systems. This paper describes the author's thesis work on creating a power-aware database management (P-DBMS) and initial ideas on the design of such systems, with the focus on a power-aware query optimization module inside the DBMS. We discuss the main technical challenges in designing the optimizer and present our strategies to meet such challenges. We focus our discussions on a power model to accurately measure the energy costs of query executions plans, and a cost evaluation model for plan selection. An important feature of this work is the formal control-theoretic methods we use to model and optimize the database towards the performance and energy saving goals. This rigorous design methodology is in sharp contrast to heuristic-based adaptive solutions that rely on extensive empirical evaluation and manual tuning. Our experiments using a power-aware query optimizer under our initial design show that there exist significant potential in power/energy savings.
---
paper_title: Towards Eco-friendly Database Management Systems

paper_content:

Database management systems (DBMSs) have largely ignored the task of managing the energy consumed during query processing. Both economical and environmental factors now require that DBMSs pay close attention to energy consumption. In this paper we approach this issue by considering energy consumption as a first-class performance goal for query processing in a DBMS. We present two concrete techniques that can be used by a DBMS to directly manage the energy consumption. Both techniques trade energy consumption for performance. The first technique, called PVC, leverages the ability of modern processors to execute at lower processor voltage and frequency. The second technique, called QED, uses query aggregation to leverage common components of queries in a workload. Using experiments run on a commercial DBMS and MySQL, we show that PVC can reduce the processor energy consumption by 49% of the original consumption while increasing the response time by only 3%. On MySQL, PVC can reduce energy consumption by 20% with a response time penalty of only 6%. For simple selection queries with no predicate overlap, we show that QED can be used to gracefully trade response time for energy, reducing energy consumption by 54% for a 43% increase in average response time. In this paper we also highlight some research issues in the emerging area of energy-efficient data processing.
---
paper_title: Analyzing the energy efficiency of a database server

paper_content:

Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization. We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.
---
paper_title: Exploring power-performance tradeoffs in database systems

paper_content:

With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11% - 22% can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements.1
---
paper_title: Energy Efficiency: The New Holy Grail of Data Management Systems Research

paper_content:

Energy costs are quickly rising in large-scale data centers and are soon projected to overtake the cost of hardware. As a result, data center operators have recently started turning into using more energy-friendly hardware. Despite the growing body of research in power management techniques, there has been little work to date on energy efficiency from a data management software perspective. In this paper, we argue that hardware-only approaches are only part of the solution, and that data management software will be key in optimizing for energy efficiency. We discuss the problems arising from growing energy use in data centers and the trends that point to an increasing set of opportunities for software-level optimizations. Using two simple experiments, we illustrate the potential of such optimizations, and, motivated by these examples, we discuss general approaches for reducing energy waste. Lastly, we point out existing places within database systems that are promising for energy-efficiency optimizations and urge the data management systems community to shift focus from performance-oriented research to energy-efficient computing.
---
paper_title: Energy conservation in wireless sensor networks: A survey

paper_content:

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.
---
paper_title: MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System

paper_content:

Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very few have promoted a distributed approach to real-time location monitoring. In this paper we present a distributed and scalable solution to processing continuously moving queries on moving objects and describe the design of MobiEyes, a distributed real-time location monitoring system in a mobile environment. Mobieyes utilizes the computational power at mobile objects, leading to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information at the server. We introduce a set of optimization techniques, such as Lazy Query Propagation, Query Grouping, and Safe Periods, to constrict the amount of computations handled by the moving objects and to enhance the performance and system utilization of Mobieyes. We also provide a simulation model in a mobile setup to study the scalability of the MobiEyes distributed location monitoring approach with regard to server load, messaging cost, and amount of computation required on the mobile objects.
---
paper_title: A framework of energy efficient mobile sensing for automatic user state recognition

paper_content:

Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices consumes huge amount of energy. In this paper, we present a novel design framework for an Energy Efficient Mobile Sensing System (EEMSS). EEMSS uses hierarchical sensor management strategy to recognize user states as well as to detect state transitions. By powering only a minimum set of sensors and using appropriate sensor duty cycles EEMSS significantly improves device battery life. We present the design, implementation, and evaluation of EEMSS that automatically recognizes a set of users' daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that our approach increases the device battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.
---
paper_title: Energy-efficient rate-adaptive GPS-based positioning for smartphones

paper_content:

Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy. In this paper, we present RAPS, rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine when to turn on GPS. It uses the location-time history of the user to estimate user velocity and adaptively turn on GPS only if the estimated uncertainty in position exceeds the accuracy threshold. It also efficiently estimates user movement using a duty-cycled accelerometer, and utilizes Bluetooth communication to reduce position uncertainty among neighboring devices. Finally, it employs celltower-RSS blacklisting to detect GPS unavailability (e.g., indoors) and avoid turning on GPS in these cases. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone lifetimes by more than a factor of 3.8 over an approach where GPS is always on.
---
paper_title: A distributed spatial index for error-prone wireless data broadcast

paper_content:

Information is valuable to users when it is available not only at the right time but also at the right place. To support efficient location-based data access in wireless data broadcast systems, a distributed spatial index (called DSI) is presented in this paper. DSI is highly efficient because it has a linear yet fully distributed structure that naturally shares links in different search paths. DSI is very resilient to the error-prone wireless communication environment because interrupted search operations based on DSI can be resumed easily. It supports search algorithms for classical location-based queries such as window queries and kNN queries in both of the snapshot and continuous query modes. In-depth analysis and simulation-based evaluation have been conducted. The results show that DSI significantly out-performs a variant of R-trees tailored for wireless data broadcast environments.
---
paper_title: SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments

paper_content:

Proactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context monitoring with rich sensors imposes heavy workloads on mobile devices with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.
---
paper_title: Power-aware operator placement and broadcasting of continuous query results

paper_content:

Complex event detection over data streams has become ubiquitous through the widespread use of sensors, wireless connectivity and the wide variety of end-user mobile devices. Typically, such event detection is carried out by a data stream management server executing continuous queries, previously submitted by the users. In this paper, we consider the situation where the end-users submit queries from hand-held devices and the results of the continuous queries, which are in the form of individual data streams, are disseminated to the users over a shared broadcast medium. Specifically, we propose three power-aware query operator placement algorithms that determine which part of a continuous query plan is executed at the data stream management server and which part is executed at the users' wireless device. The algorithms' effectiveness with respect to energy consumption is evaluated using simulation.
---
paper_title: The ubiquitous DBMS

paper_content:

Recent widespread use of mobile technologies and advancement in computing power prompted strong needs of database systems that can be used in small devices such as sensors, cellular phones, PDA, ultra PCs, and navigators. We call database systems that are customizable from small-scale applications for small devices to large-scale applications such as large-scale search engines ubiquitous database management systems (UDBMSs). In this talk, we first review requirements of UDBMSs. The requirements we identified include selective convergence (or "devicetization"), flash-optimized storage systems, data synchronization, supportability of unstructured/semi-structured data, and complex database operations. We then review existing systems and research prototypes. We first review the functionality of UDBMSs including the footprint size, support of standard SQL, supported data types, transactions, concurrency control, indexing, and recovery. We then review the supportability of requirements by those UDBMSs surveyed. We highlight ubiquitous features of a family of Odysseus systems that have been under development at KAIST for over 20 years. Functionalities of Odysseus can be "devicetized" or customized depending on the device types and applications as in Odysseus/Mobile for small devices, Odysseus/XML for unstructured/semistructured data, Odysseus/GIS for map data, and Odysseus/IR for large-scale search engines. We finally present research topics that are related to the UDBMSs.
---
paper_title: Energy- and Latency-Efficient Processing of Full-Text Searches on a Wireless Broadcast Stream

paper_content:

In wireless mobile computing environments, broadcasting is an effective and scalable technique to disseminate information to a massive number of clients, wherein the energy usage and latency are considered major concerns. This paper presents an indexing scheme for the energy- and latency-efficient processing of full-text searches over the wireless broadcast data stream. Although a lot of access methods and index structures have been proposed in the past for full-text searches, all of them are targeted for data in disk storage, not wireless broadcast channels. For full-text searches on a wireless broadcast stream, we firstly introduce a naive, inverted list-style indexing method, where inverted lists are placed in front of the data on the wireless channel. In order to reduce the latency overhead, we propose a two-level indexing method which adds another level of index structure to the basic inverted list-style index. In addition, we propose a replication strategy of the index list and index tree to further improve the latency performance. We analyze the performance of the proposed indexing scheme with respect to the latency and energy usage measures, and show the optimality of index replication. The correctness of the analysis is demonstrated through simulation experiments, and the effectiveness of the proposed scheme is shown by implementing a real wireless information delivery system.
---
paper_title: Energy efficient index for querying location-dependent data in mobile broadcast environments

paper_content:

We are witnessing in recent years growing interest for location-dependent information services among mobile users. We examine the issue of processing location-dependent queries in a mobile broadcast environment. Different from a traditional environment, mobile users are concerned with not only access latencies but also power conservation. The planar point location algorithms and conventional spatial index structures are shown inefficient. We propose a new index data structure, called D-tree, for querying location-dependent data in mobile broadcast environments. The basic idea is to index data regions based on the divisions between them. We describe how to construct the binary D-tree index, how to process location-dependent queries based on this index structure, and how to page the D-tree to fit the packet capacity. The performance of the D-tree is evaluated using both synthetic and real datasets. Experimental results show that the proposed D-tree provides a much better overall performance than the well-known existing schemes such as the R*-tree.
---
paper_title: EnTracked: energy-efficient robust position tracking for mobile devices

paper_content:

An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, tracking has to robustly deliver position updates when faced with changing conditions such as delays due to positioning and communication, and changing positioning accuracy. This work proposes EnTracked - a system that, based on the estimation and prediction of system conditions and mobility, schedules position updates to both minimize energy consumption and optimize robustness. The realized system tracks pedestrian targets equipped with GPS-enabled devices. The system is configurable to realize different trade-offs between energy consumption and robustness. We provide extensive experimental results by profiling how devices consume power, by emulation on collected data and by validation in several real-world deployments. Results from this profiling show how a device consumes power while tracking its position. Results from the emulation indicate that the system can estimate and predict system conditions and mobility. Furthermore they provide evidence for that the system can lower the energy consumption considerably and remain robust when faced with changing system conditions. By validation in several real-world deployments we provide evidence that the real system works as predicted by the emulation.
---
paper_title: Online performance analysis by statistical sampling of microprocessor performance counters

paper_content:

Hardware performance counters (HPCs) are increasingly being used to analyze performance and identify the causes of performance bottlenecks. However, HPCs are difficult to use for several reasons. Microprocessors do not provide enough counters to simultaneously monitor the many different types of events needed to form an over-all understanding of performance. Moreover, HPCs primarily count low-level micro-architectural events from which it is difficult to extract high-level insight required for identifying causes of performance problems.We describe two techniques that help overcome these difficulties, allowing HPCs to be used in dynamic real-time optimizers. First, statistical sampling is used to dynamically multiplex HPCs and make a larger set of logical HPCs available. Using real programs, we show experimentally that it is possible through this sampling to obtain counts of hardware events that are statistically similar (within 15%) to complete non-sampled counts, thus allowing us to provide a much larger set of logical HPCs. Second, we observe that stall cycles are a primary source of inefficiencies, and hence they should be major targets for software optimization. Based on this observation, we build a simple model in real-time that speculatively associates each stall cycle to a processor component that likely caused the stall. The information needed to produce this model is obtained using our HPC multiplexing facility to monitor a large number of hardware components simultaneously. Our analysis shows that even in an out-of-order superscalar micro-processor such a speculative approach yields a fairly accurate model with run-time overhead for collection and computation of under 2%.These results demonstrate that we can effective analyze on-line performance of application and system code running at full speed. The stall analysis shows where performance is being lost on a given processor.
---
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%.
---
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.
---
paper_title: Energy Management for MapReduce Clusters

paper_content:

The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload response time and overall energy consumption. Since the MapReduce framework is becoming "ubiquitous", the focus of this paper is on developing a framework for systematically considering various MapReduce node power down strategies, and their impact on the overall energy consumption and workload response time. ::: ::: We closely examine two extreme techniques that can be accommodated in this framework. The first is based on a recently proposed technique called "Covering Set" (CS) that keeps only a small fraction of the nodes powered up during periods of low utilization. At the other extreme is a technique that we propose in this paper, called the All-In Strategy (AIS). AIS uses all the nodes in the cluster to run a workload and then powers down the entire cluster. Using both actual evaluation and analytical modeling we bring out the differences between these two extreme techniques and show that AIS is often the right energy saving strategy.
---
paper_title: Complete System Power Estimation: A Trickle-Down Approach Based on Performance Events

paper_content:

This paper proposes the use of microprocessor performance counters for online measurement of complete system power consumption. While past studies have demonstrated the use of performance counters for microprocessor power, to the best of our knowledge, we are the first to create power models for the entire system based on processor performance events. Our approach takes advantage of the "trickle-down" effect of performance events in a microprocessor. We show how well known performance-related events within a microprocessor such as cache misses and DMA transactions are highly correlated to power consumption outside of the microprocessor. Using measurement of an actual system running scientific and commercial workloads we develop and validate power models for five subsystems: memory, chipset, I/O, disk and microprocessor. These models are shown to have an average error of less than 9% per subsystem across the considered workloads. Through the use of these models and existing on-chip performance event counters, it is possible to estimate system power consumption without the need for additional power sensing hardware
---
paper_title: Using complete machine simulation for software power estimation: the SoftWatt approach

paper_content:

Power dissipation has become one of the most critical factors for the continued development of both high-end and low-end computer systems. We present a complete system power simulator, called SoftWatt, that models the CPU, memory hierarchy, and a low-power disk subsystem and quantifies the power behavior of both the application and operating system. This tool, built on top of the SimOS infrastructure, uses validated analytical energy models to identify the power hotspots in the system components, capture relative contributions of the user and kernel code to the system power profile, identify the power-hungry operating system services and characterize the variance in kernel power profile with respect to workload. Our results using Spec JVM98 benchmark suite emphasize the importance of complete system simulation to understand the power impact of architecture and operating system on application execution.
---
paper_title: Run-time power estimation in high performance microprocessors

paper_content:

Power concerns are becoming increasingly pressing in high-performance processors. Building power-aware and even power-adaptive computer architectures requires being able to track power consumption and attribute energy consumption to the portions of the chip that are responsible for it. This paper presents the Castle project which aims to deduce the actual runtime power dissipated by different processor units on the CPU chip by leveraging existing hardware. Namely, we examine the use of hardware performance counters as proxies for power meters. We discuss which performance counters count power-relevant events, and how to estimate event counts for power-relevant events not well supported by current, commonly available performance counters. We also discuss sampling-based approaches for estimating signal transition activity within the processor. Overall, we find that these performance counters can be quite useful in providing good power apportionment estimates for programs as they run.
---
paper_title: Exploring power-performance tradeoffs in database systems

paper_content:

With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11% - 22% can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements.1
---
paper_title: A Comparison of High-Level Full-System Power Models

paper_content:

Dynamic power management in enterprise environments requires an understanding of the relationship between resource utilization and system-level power consumption. Power models based on resource utilization have been proposed in the context of enabling specific energy-efficiency optimizations on specific machines, but the accuracy and portability of different approaches to modeling have not been systematically compared. In this work, we use a common infrastructure to fit a family of high-level full-system power models, and we compare these models over a wide variation of workloads and machines, from a laptop to a server. This analysis shows that a model based on OS utilization metrics and CPU performance counters is generally most accurate across the machines and workloads tested. It is particularly useful for machines whose dynamic power consumption is not dominated by the CPU, as well as machines with aggressively power-managed CPUs, two classes of systems that are increasingly prevalent.
---
paper_title: Full-system power analysis and modeling for server environments

paper_content:

The increasing costs of power delivery and cooling, as well as the trend toward higher-density computer systems, have created a growing demand for better power management in server environments. Despite the increasing interest in this issue, little work has been done in quantitatively understanding power consumption trends and developing simple yet accurate models to predict full-system power. We study the component-level power breakdown and variation, as well as temporal workload-specific power consumption of an instrumented power-optimized blade server. Using this analysis, we examine the validity of prior adhoc approaches to understanding power breakdown and quantify several interesting trends important for power modeling and management in the future. We also introduce Mantis, a nonintrusive method for modeling full-system power consumption and providing real-time power prediction. Mantis uses a onetime calibration phase to generate a model by correlating AC power measurements with user-level system utilization metrics. We experimentally validate the model on two server systems with drastically different power footprints and characteristics (a low-end blade and high-end compute-optimized server) using a variety of workloads. Mantis provides power estimates with high accuracy for both overall and temporal power consumption, making it a valuable tool for power-aware scheduling and analysis.
---
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.
---
paper_title: Building a power-aware database management system

paper_content:

In today's large-scale data centers, energy costs (i.e., the electricity bill) are projected to outgrow that of hardware. Despite a long history of research in energy-saving techniques, especially low-power hardware, little work has been done to improve the power efficiency of data management software. Power-aware computing research at the application level has been found to be synergistic to that at the hardware and OS levels because it can provide more opportunities for energy reduction in the underlying systems. This paper describes the author's thesis work on creating a power-aware database management (P-DBMS) and initial ideas on the design of such systems, with the focus on a power-aware query optimization module inside the DBMS. We discuss the main technical challenges in designing the optimizer and present our strategies to meet such challenges. We focus our discussions on a power model to accurately measure the energy costs of query executions plans, and a cost evaluation model for plan selection. An important feature of this work is the formal control-theoretic methods we use to model and optimize the database towards the performance and energy saving goals. This rigorous design methodology is in sharp contrast to heuristic-based adaptive solutions that rely on extensive empirical evaluation and manual tuning. Our experiments using a power-aware query optimizer under our initial design show that there exist significant potential in power/energy savings.
---
paper_title: Energy benchmarks: a detailed analysis

paper_content:

In light of an increase in energy cost and energy consciousness industry standard organizations such as Transaction Processing Performance Council (TPC), Standard Performance Evaluation Corporation (SPEC) and Storage Performance Council (SPC) as well as the U.S. Environmental Protection Agency have developed tests to measure energy consumption of computer systems. Although all of these consortia aim at standardizing power consumption measurement using benchmarks, ultimately aiming to reduce overall power consumption, and to aid in making purchase decisions, their methodologies differ slightly. For instance, some organizations developed specialized benchmarks while others added energy metrics to existing benchmarks. In this paper we give a comprehensive overview of the currently available energy benchmarks followed by an in depth analysis of their commonalities and differences.
---
paper_title: JouleSort: a balanced energy-efficiency benchmark

paper_content:

The energy efficiency of computer systems is an important concern in a variety of contexts. In data centers, reducing energy use improves operating cost, scalability, reliability, and other factors. For mobile devices, energy consumption directly affects functionality and usability. We propose and motivate JouleSort, an external sort benchmark, for evaluating the energy efficiency of a wide range of computer systems from clusters to handhelds. We list the criteria, challenges, and pitfalls from our experience in creating a fair energy-efficiency benchmark. Using a commercial sort, we demonstrate a JouleSort system that is over 3.5x as energy-efficient as last year's estimated winner. This system is quite different from those currently used in data centers. It consists of a commodity mobile CPU and 13 laptop drives connected by server-style I/O interfaces.
---

Title: A Survey on Energy-Efficient Data Management

Section 1: INTRODUCTION
Description 1: In this section, introduce the concept of green computing and its significance. Discuss the energy efficiency challenges in computing and detail the focus of the paper on energy-efficient data management.

Section 2: FUNDAMENTALS OF ENERGY-EFFICIENT COMPUTING
Description 2: Describe the energy-efficiency problem and provide some guidelines and solutions to tackle the problem. Explain the approaches to enhance energy efficiency in computing systems.

Section 3: ENERGY-EFFICIENT DATA MANAGEMENT
Description 3: Highlight the importance of energy-efficient database management systems and describe efforts on energy-efficient data management at server, sensor, and mobile sides.

Section 4: Server Side
Description 4: Discuss research and methodologies for energy-efficient data management on the server side, including hardware-based and software-based approaches.

Section 5: Sensor Side
Description 5: Explain the techniques for energy-efficient data management on the sensor side, focusing on duty cycling, data-driven schemes, and mobility-based schemes.

Section 6: Mobile Side
Description 6: Describe the approaches for energy-efficient data management in mobile devices, including position tracking, continuous context monitoring, and complex event detection.

Section 7: EVALUATION METHODS
Description 7: Discuss the metrics and models for evaluating energy-efficient computing methods. Highlight the different types of modeling approaches and benchmarks used for performance measurement.

Section 8: CONCLUSION
Description 8: Summarize the survey by reiterating the key points discussed about the energy-efficiency problem and the various strategies for energy-efficient data management.