Large-scale Distributed Systems and Energy Efficiency

A Holistic View

Inbunden, Engelska, 2015

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Addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networksWith concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. Large-Scale Distributed Systems and Energy Efficiency: A Holistic View addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. Discusses measurement and modeling of the energy consumption methodIncludes methods for energy consumption reduction in diverse computing environmentsFeatures a variety of case studies and examples of energy reduction and assessmentTimely and important, Large-Scale Distributed Systems and Energy Efficiency is an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.

Produktinformation

Utgivningsdatum2015-05-19
Mått160 x 244 x 23 mm
Vikt576 g
FormatInbunden
SpråkEngelska
SerieWiley Series on Parallel and Distributed Computing
Antal sidor336
FörlagJohn Wiley & Sons Inc
ISBN9781118864630

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Jean-Marc Pierson is a Professor in Computer Science at the University of Toulouse?(France). Jean-Marc Pierson received his PhD from the ENS-Lyon, France in1996. He was an Associate Professor at the University Littoral?Cote-d'Opale (1997-2001) in Calais, then at INSA-Lyon (2001-2006). He is a member of the IRIT Laboratory and Chair of the SEPIA Team on distributed systems. His research focuses on energy- aware distributed systems, in particular?monitoring, job placement and scheduling, green networking, autonomic computing, and mathematical modelling.

Preface xvAcknowledgment xvii1 INTRODUCTION TO ENERGY EFFICIENCY IN LARGE-SCALE DISTRIBUTED SYSTEMS 1Jean-Marc Pierson and Helmut Hlavacs1.1 Energy Consumption Status 11.2 Target of the Book 31.3 The Cost Action IC0804 41.3.1 Birth of the Action 41.3.2 Development of the Action 51.3.3 End and Future of the Action 101.4 Chapters Preview 11Acknowledgement 12References 122 HARDWARE LEVERAGES FOR ENERGY REDUCTION IN LARGE-SCALE DISTRIBUTED SYSTEMS 17Davide Careglio, Georges Da Costa, and Sergio Ricciardi2.1 Introduction 172.1.1 Motivation for Energy-Aware Distributed Computing 172.2 Processor 192.2.1 Context 192.2.2 Advanced Configuration and Power Interface (ACPI) 202.2.3 Vendors 212.2.4 General-Purpose Graphics Processing Unit (GPGPU) 232.2.5 ARM Architecture 242.3 Memory (DRAM) 252.3.1 Context 252.3.2 Power Consumption 252.3.3 Energy Efficiency Techniques 262.3.4 Vendors 262.4 Disk/Flash 272.4.1 Spindle Speed 282.4.2 Seek Speed 282.4.3 Power Modes 292.4.4 Power Consumption 292.4.5 Solid-State Drive (SDD) 292.5 Fan 302.6 Power Supply Unit 302.7 Network Infrastructure 312.7.1 Current Scenario 312.7.2 New Energy-Oriented Model 322.7.3 Current Advances in Networking 332.7.4 Adaptive Link Rate (ALR) 342.7.5 Low Power Idle (LPI) 342.7.6 Energy-Aware Dynamic RWA Framework 342.7.7 Energy-Aware Network Attacks 35References 363 GREEN WIRED NETWORKS 41Alfonso Gazo Cervero, Michele Chincoli, Lars Dittmann, Andreas Fischer, Alberto E. Garcia, Jaime Galán-Jiménez, Laurent Lefevre, Hermann de Meer, Thierry Monteil, Paolo Monti, Anne-Cecile Orgerie, Louis-Francois Pau, Chris Phillips, Sergio Ricciardi, Remi Sharrock, Patricia Stolf, Tuan Trinh, and Luca Valcarenghi3.1 Economic Incentives and Green Tariffing 443.1.1 Regulatory, Economic, and Microeconomic Measures 443.1.2 Pricing Theory in Relation to Green Policies 463.1.3 COST Action Results 503.2 Network Components 513.2.1 Router 513.2.2 Network Interface Card 553.2.3 Reconfigurable Optical Add-Drop Multiplexer 563.2.4 Digital Subscriber Line Access Multiplexer 563.3 Architectures 573.3.1 Access Networks 573.3.2 Carrier Networks 583.3.3 Grid Overlay Networks 583.4 Traffic Considerations 593.5 Energy-Saving Mechanisms 603.5.1 Static Mechanisms 603.5.2 Dynamic Mechanisms 613.6 Challenges 723.7 Summary 72References 734 GREEN WIRELESS-ENERGY EFFICIENCY IN WIRELESS NETWORKS 81Vitor Bernardo, Torsten Braun, Marilia Curado, Markus Fiedler, David Hock, Theus Hossmann, Karin Anna Hummel, Philipp Hurni, Selim Ickin, Almerima Jamakovic-Kapic, Simin Nadjm-Tehrani, Tuan Ahn Trinh, Ekhiotz Jon Vergara, Florian Wamser, and Thomas Zinner4.1 Introduction 814.2 Metrics and Trade-Offs in Wireless Networks 834.2.1 Metrics 834.2.2 Energy Optimization Trade-Offs 844.2.3 Summary 854.3 Measurement Methodology 854.3.1 Energy Measurement Testbeds 864.3.2 Energy Estimation Techniques 904.3.3 Energy Measurements versus Estimation 974.3.4 Summary 994.4 Energy Efficiency and QoE in Wireless Access Networks 1004.4.1 Energy Issues in Cellular Networks 1004.4.2 Energy Efficiency and QoE in Wireless Mesh Networks 1014.4.3 Reducing Energy Consumption of the End User Device 1054.4.4 Energy Measurements Revealing Video QoE Issues 1084.4.5 Energy Issues in Environmental WMNs 1104.4.6 Summary 1124.5 Energy-Efficient Medium Access in Wireless Sensor Networks 1134.5.1 MaxMAC – An Energy-Efficient MAC Protocol 1134.5.2 Real-World Testbed Experiments with MaxMAC 1164.5.3 Summary 1194.6 Energy-Efficient Connectivity in Ad-Hoc and Opportunistic Networks 1194.6.1 Ad-Hoc Networking 1204.6.2 Opportunistic and Delay-Tolerant Networking 1214.6.3 Summary 1234.7 Summary and Conclusions 124References 1255 POWER MODELING 131Jason Mair, Zhiyi Huang, David Eyers, Leandro Cupertino, Georges Da Costa, Jean-Marc Pierson, and Helmut Hlavacs5.1 Introduction 1315.2 Measuring Power 1335.2.1 External Power Meters 1335.2.2 Internal Power Meters 1345.3 Performance Indicators 1355.3.1 Source Instrumentation 1355.3.2 Binary Instrumentation 1365.3.3 Performance Monitoring Counters 1365.3.4 Operating System Events 1375.3.5 Virtual Machine Performance 1385.4 Interaction between Power and Performance 1385.4.1 Central Processing Unit (CPU) 1385.4.2 Memory 1405.4.3 Input/Output (I/O) 1415.4.4 Network 1415.4.5 Idle States 1425.5 Power Modeling Procedure 1435.5.1 Variable Selection 1435.5.2 Training Data Collection 1445.5.3 Learning from Data 1455.5.4 Event Correlation 1455.5.5 Model Evaluation Concepts 1465.5.6 Power Estimation Errors 1485.5.7 Related Work 1495.6 Use-Cases 1515.6.1 Applications 1515.6.2 Single-Core Systems 1525.6.3 Multi-core and Multiprocessor 1525.6.4 Distributed Systems 1535.7 Available Software 1545.8 Conclusion 155References 1566 GREEN DATA CENTERS 159Robert Basmadjian, Pascal Bouvry, Georges Da Costa, László Gyarmati, Dzmitry Kliazovich, Sébastien Lafond, Laurent Lefèvre, Hermann De Meer, Jean-Marc Pierson, Rastin Pries, Jordi Torres, Tuan Anh Trinh, and Samee Ullah Khan6.1 Introduction 1606.2 Overview of Energy Consumption of Hardware Infrastructure in Data Center 1616.2.1 Energy Consumption Rankings and Metrics 1616.2.2 Processing: CPU, GPU, and memory 1626.2.3 Storage 1686.2.4 Communicating Elements 1686.3 Middleware Solutions that Regulate and Optimize the Energy Consumption in Data Centers 1696.3.1 An Overview of the Middleware 1696.3.2 System Modeling 1716.3.3 Control Mechanisms 1726.3.4 A Use Case of Leveraging Energy Efficiency in Data Centers 1746.4 Data Center Network Architectures 1776.4.1 Architectures 1776.4.2 Power Consumption of Data Center Architectures 1816.4.3 Additional Proposals for Energy-Efficient Data Centers 1826.5 Solutions for Cooling and Heat Control in Data Center 1846.5.1 Mechanical-Based Approaches 1856.5.2 Software-Based Approaches 187Acknowledgments 187References 1887 ENERGY EFFICIENCY AND HIGH-PERFORMANCE COMPUTING 197Pascal Bouvry, Ghislain Landry Tsafack Chetsa, Georges Da Costa, Emmanuel Jeannot, Laurent Lefèvre, Jean-Marc Pierson, Frédéric Pinel, Patricia Stolf, and Sébastien Varrette7.1 Introduction 1977.2 Overview of HPC Components and Latest Trends Toward Energy Efficiency 1987.2.1 Architecture of the Current HPC Facilities 1987.2.2 Overview of the Main HPC Components 2017.2.3 HPC Performance and Energy Efficiency Evaluation 2037.3 Building the Path to Exascale Computing 2067.3.1 The Exascale Challenge: Hardware and Architecture Issues 2067.3.2 Energy Efficiency and Resource and Job Management System (RJMS) 2077.3.3 Energy-Aware Software 2107.3.4 A Methodology for Energy Reduction in HPC 2107.4 Energy Efficiency of Virtualization and Cloud Frameworks over HPC Workloads 2167.5 Conclusion: Open Challenges 221Acknowledgments 222References 2228 SCHEDULING AND RESOURCE ALLOCATION 225Pragati Agrawal, Damien Borgetto, Carmela Comito, Georges Da Costa, Jean-Marc Pierson, Payal Prakash, Shrisha Rao, Domenico Talia, Cheikhou Thiam, and Paolo Trunfio8.1 Introduction: Energy-Aware Scheduling 2258.2 Use of Linear Programming in Energy-Aware Scheduling 2268.2.1 Finding the Optimal Solution Using a Linear Program 2268.2.2 Benefits and Limitations of LP 2278.3 Heuristics in Large Instances 2288.3.1 Energy-Aware Greedy Algorithms 2298.3.2 Vector Packing 2298.3.3 Improving Fast Algorithms 2298.4 Comparing Allocation Heuristics for Energy-Aware Scheduling 2308.4.1 Problem Formulation 2308.4.2 Allocation Heuristics 2328.4.3 Results 2348.5 Energy-Aware Task Allocation in Mobile Environments 2368.5.1 Reference Architecture 2378.5.2 Task Allocation Strategy 2388.5.3 Task Allocation Algorithm 2398.5.4 Performance Results 2418.6 An Energy-Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 2438.6.1 Decentralized Resources in Cloud: Overview 2438.6.2 Cooperative Scheduling Anti-Load Balancing Algorithm for Cloud (CSAAC) 2448.6.3 Simulation Results 2458.6.4 Evaluation 2488.7 Cost-Aware Scheduling with Smart Grids 2488.7.1 Cost-Aware Scheduling 2488.7.2 Cost-Aware Scheduling Using DE 2528.7.3 Comparison of DE with Other Approaches 2548.8 Heterogeneity, Cooling, DVFS, and Migration 2578.8.1 Lever Interactions 2578.8.2 Infrastructures 2578.8.3 Resource Allocation as a Whole 2588.9 Conclusions 259References 2609 ENERGY EFFICIENCY IN P2P SYSTEMS AND APPLICATIONS 263Simone Brienza, Sena Efsun Cebeci, Seyed-Saeid Masoumzadeh, Helmut Hlavacs, Öznur Özkasap, Giuseppe Anastasi9.1 Introduction 2649.2 General Approaches to Energy Efficiency 2649.2.1 Sleep/Wakeup Approaches 2649.2.2 Hierarchical Approaches 2669.2.3 Resource Allocation 2689.3 Energy Efficiency in File-Sharing Applications 2699.3.1 Client–Server versus P2P File Sharing 2699.3.2 Energy Efficiency in P2P File Sharing 2709.3.3 Energy Efficiency in BitTorrent 2709.3.4 Energy Efficiency in Other File-Sharing Protocols 2799.4 Energy Efficiency in P2P Epidemic Protocols 2809.5 Conclusions 282References 28310 TOWARD SUSTAINABILITY FOR LARGE-SCALE COMPUTING SYSTEMS: ENVIRONMENTAL, ECONOMIC, AND STANDARDIZATION ASPECTS 287Christina Herzog, Jean-Marc Pierson, and Laurent Lefèvre10.1 Introduction 28710.2 Green IT for Innovation and Innovation for Green IT 28810.2.1 Defining Green IT and Its Link with Sustainability 28810.2.2 Differences between Academia and Companies 29110.2.3 Describing the Loop between Academia and Industry 29410.3 Standardization Landscape in Green IT 29510.3.1 Different Standardization Levels 29610.3.2 Standardization Bodies 29710.3.3 Regulations 29910.3.4 Industry Groups and Professional Bodies 29910.3.5 Analysis of the Standardization Actors 30110.4 Modeling Actors of Innovation in Green IT and their Links 30110.4.1 Researcher 30110.4.2 Universities 30210.4.3 Technology Transfer Office (TTO) 30210.4.4 Industry 30210.4.5 Funding Organization 30310.4.6 Standardization Body 30310.4.7 Links between Actors 30310.4.8 Rating the Relationships between Actors 30410.5 Using the Modeling for Deciding 30610.5.1 Methodology to be Developed 30610.6 Conclusion 307Acknowledgment 307References 307Author Index 309Subject Index 311