A survey of design techniques for system-level dynamic power management
01 Jun 2000-IEEE Transactions on Very Large Scale Integration Systems (Kluwer Academic Publishers)-Vol. 8, Iss: 3, pp 231-248
TL;DR: This paper describes how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption, and survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components.
Abstract: Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited) In this paper, we survey several approaches to system-level dynamic power management We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components
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TL;DR: This study discusses causes and problems of high power/energy consumption, and presents a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization, and data center levels.
Abstract: Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific, and business domains. However, the ever-increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements, it is essential to synthesize and classify the research on power- and energy-efficient design conducted to date. In this study, we discuss causes and problems of high power/energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization, and data center levels. We survey various key works in the area and map them onto our taxonomy to guide future design and development efforts. This chapter concludes with a discussion on advancements identified in energy-efficient computing and our vision for future research directions.
745 citations
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TL;DR: There is a growing interest in Networks on Chips (NoC) that is related to the evolution of integrated circuit technology and to the growing requirements in performance and portability of electronic systems.
Abstract: We are witnessing a growing interest in Networks on Chips (NoC) that is related to the evolution of integrated circuit technology and to the growing requirements in performance and portability of electronic systems. Current integrated circuits contain several processing cores, and even relatively simple systems, such as cellular telephones, behave as multiprocessors. Moreover, many electronic systems consist of heterogeneous components and they require efficient on-chip communication. In the last few years, multiprocessing platforms have been developed to address high performance computation, such as image rendering. Examples are Sony’s emotion engine [OKA] and IBM’s cell chip [PHAM] where on-chip communication efficiency is key to the overall system performance.
641 citations
28 May 2007
TL;DR: This tutorial presents an overview of model checking for both discrete and continuous-time Markov chains (DTMCs and CTMCs) by outlining the main features supported by PRISM and three real-world case studies: a probabilistic security protocol, dynamic power management and a biological pathway.
Abstract: This tutorial presents an overview of model checking for both discrete and continuous-time Markov chains (DTMCs and CTMCs). Model checking algorithms are given for verifying DTMCs and CTMCs against specifications written in probabilistic extensions of temporal logic, including quantitative properties with rewards. Example properties include the probability that a fault occurs and the expected number of faults in a given time period. We also describe the practical application of stochastic model checking with the probabilistic model checker PRISM by outlining the main features supported by PRISM and three real-world case studies: a probabilistic security protocol, dynamic power management and a biological pathway.
630 citations
TL;DR: Algorithmic solutions can help reduce energy consumption in computing environs by automating the very labor-intensive and therefore time-heavy and expensive process of designing and implementing algorithms.
Abstract: Algorithmic solutions can help reduce energy consumption in computing environs.
436 citations
07 Jan 2002
TL;DR: An introduction to this emerging area of battery modeling and battery-efficient system design is presented, promising technologies that have been developed are surveyed, and emerging industry standards for smart battery systems are outlined.
Abstract: As an increasing number of electronic systems are powered by batteries, battery life becomes a primary design consideration. Maxiimizing battery life requires system designers to develop an understanding of the capabilities and limitations of the batteries that power such systems, and to incorporate battery considerations into the system design process. Recent research has shown that, the amount of energy that can be supplied by a given battery varies significantly, depending on how the energy is drawn. Consequently, researchers are attempting to develop new battery-driven approaches to system design, which deliver battery life improvements over and beyond what can be achieved through conventional low-power design techniques. This paper presents an introduction to this emerging area, surveys promising technologies that have been developed for battery modeling and battery-efficient system design, and outlines emerging industry standards for smart battery systems.
388 citations
Cites background from "A survey of design techniques for s..."
...While system-level power management is in itself a wellresearched area ( [6], [31]), recent research has proposed new power management schemes that specifically target batteryefficiency rather than average power....
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References
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TL;DR: Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.
Abstract: From the Publisher:
The past decade has seen considerable theoretical and applied research on Markov decision processes, as well as the growing use of these models in ecology, economics, communications engineering, and other fields where outcomes are uncertain and sequential decision-making processes are needed. A timely response to this increased activity, Martin L. Puterman's new work provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models. It discusses all major research directions in the field, highlights many significant applications of Markov decision processes models, and explores numerous important topics that have previously been neglected or given cursory coverage in the literature. Markov Decision Processes focuses primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous-time discrete state models. The book is organized around optimality criteria, using a common framework centered on the optimality (Bellman) equation for presenting results. The results are presented in a \"theorem-proof\" format and elaborated on through both discussion and examples, including results that are not available in any other book. A two-state Markov decision process model, presented in Chapter 3, is analyzed repeatedly throughout the book and demonstrates many results and algorithms. Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria. It also explores several topics that have received little or no attention in other books, including modified policy iteration, multichain models with average reward criterion, and sensitive optimality. In addition, a Bibliographic Remarks section in each chapter comments on relevant historic
5,188 citations
TL;DR: There is a comprehensive introduction to the applied models of probability that stresses intuition, and both professionals, researchers, and the interested reader will agree that this is the most solid and widely used book for probability theory.
Abstract: The Seventh Edition of the successful Introduction to Probability Models introduces elementary probability theory and stochastic processes. This book is particularly well-suited to those applying probability theory to the study of phenomena in engineering, management science, the physical and social sciences, and operations research. Skillfully organized, Introduction to Probability Models covers all essential topics. Sheldon Ross, a talented and prolific textbook author, distinguishes this book by his effort to develop in students an intuitive, and therefore lasting, grasp of probability theory. Ross' classic and best-selling text has been carefully and substantially revised. The Seventh Edition includes many new examples and exercises, with the majority of the new exercises being of the easier type. Also, the book introduces stochastic processes, stressing applications, in an easily understood manner. There is a comprehensive introduction to the applied models of probability that stresses intuition. Both professionals, researchers, and the interested reader will agree that this is the most solid and widely used book for probability theory. Features: * Provides a detailed coverage of the Markov Chain Monte Carlo methods and Markov Chain covertimes * Gives a thorough presentation of k-record values and the surprising Ignatov's * theorem * Includes examples relating to: "Random walks to circles," "The matching rounds problem," "The best prize problem," and many more * Contains a comprehensive appendix with the answers to approximately 100 exercises from throughout the text * Accompanied by a complete instructor's solutions manual with step-by-step solutions to all exercises New to this edition: * Includes many new and easier examples and exercises * Offers new material on utilizing probabilistic method in combinatorial optimization problems * Includes new material on suspended animation reliability models * Contains new material on random algorithms and cycles of random permutations
4,945 citations
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01 Jan 1972
TL;DR: The introduction to probability models eighth edition is universally compatible with any devices to read and an online access to it is set as public so you can get it instantly.
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727 citations
"A survey of design techniques for s..." refers methods in this paper
...More specifically [39], power management optimization has been studied within the framework of controlled Markov processes[ 42 ], [43]....
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Journal Article•
691 citations
TL;DR: A finite-state, abstract system model for power-managed systems based on Markov decision processes is introduced and the problem of finding policies that optimally tradeoff performance for power can be cast as a stochastic optimization problem and solved exactly and efficiently.
Abstract: Dynamic power management schemes (also called policies) reduce the power consumption of complex electronic systems by trading off performance for power in a controlled fashion, taking system workload into account. In a power-managed system it is possible to set components into different states, each characterized by performance and power consumption levels. The main function of a power management policy is to decide when to perform component state transitions and which transition should be performed, depending on system history, workload, and performance constraints. In the past, power management policies have been formulated heuristically. The main contribution of this paper is to introduce a finite-state, abstract system model for power-managed systems based on Markov decision processes. Under this model, the problem of finding policies that optimally tradeoff performance for power can be cast as a stochastic optimization problem and solved exactly and efficiently. The applicability and generality of the approach are assessed by formulating the Markov model and optimizing power management policies for several systems.
459 citations