{"product_id":"large-scale-distributed-systems-and-energy-efficiency-isbn-9781118864630","title":"Large-scale Distributed Systems and Energy Efficiency","description":"\u003cp\u003e\u003cb\u003eAddresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. \u003ci\u003eLarge-Scale Distributed Systems and Energy Efficiency: A Holistic View\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDiscusses measurement and modeling of the energy consumption method\u003c\/li\u003e \u003cli\u003eIncludes methods for energy consumption reduction in diverse computing environments\u003c\/li\u003e \u003cli\u003eFeatures a variety of case studies and examples of energy reduction and assessment\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTimely and important, \u003ci\u003eLarge-Scale Distributed Systems and Energy Efficiency \u003c\/i\u003eis an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgment xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 INTRODUCTION TO ENERGY EFFICIENCY IN LARGE-SCALE DISTRIBUTED SYSTEMS 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJean-Marc Pierson and Helmut Hlavacs\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Energy Consumption Status 1\u003c\/p\u003e \u003cp\u003e1.2 Target of the Book 3\u003c\/p\u003e \u003cp\u003e1.3 The Cost Action IC0804 4\u003c\/p\u003e \u003cp\u003e1.3.1 Birth of the Action 4\u003c\/p\u003e \u003cp\u003e1.3.2 Development of the Action 5\u003c\/p\u003e \u003cp\u003e1.3.3 End and Future of the Action 10\u003c\/p\u003e \u003cp\u003e1.4 Chapters Preview 11\u003c\/p\u003e \u003cp\u003eAcknowledgement 12\u003c\/p\u003e \u003cp\u003eReferences 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 HARDWARE LEVERAGES FOR ENERGY REDUCTION IN LARGE-SCALE DISTRIBUTED SYSTEMS 17\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDavide Careglio, Georges Da Costa, and Sergio Ricciardi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 17\u003c\/p\u003e \u003cp\u003e2.1.1 Motivation for Energy-Aware Distributed Computing 17\u003c\/p\u003e \u003cp\u003e2.2 Processor 19\u003c\/p\u003e \u003cp\u003e2.2.1 Context 19\u003c\/p\u003e \u003cp\u003e2.2.2 Advanced Configuration and Power Interface (ACPI) 20\u003c\/p\u003e \u003cp\u003e2.2.3 Vendors 21\u003c\/p\u003e \u003cp\u003e2.2.4 General-Purpose Graphics Processing Unit (GPGPU) 23\u003c\/p\u003e \u003cp\u003e2.2.5 ARM Architecture 24\u003c\/p\u003e \u003cp\u003e2.3 Memory (DRAM) 25\u003c\/p\u003e \u003cp\u003e2.3.1 Context 25\u003c\/p\u003e \u003cp\u003e2.3.2 Power Consumption 25\u003c\/p\u003e \u003cp\u003e2.3.3 Energy Efficiency Techniques 26\u003c\/p\u003e \u003cp\u003e2.3.4 Vendors 26\u003c\/p\u003e \u003cp\u003e2.4 Disk\/Flash 27\u003c\/p\u003e \u003cp\u003e2.4.1 Spindle Speed 28\u003c\/p\u003e \u003cp\u003e2.4.2 Seek Speed 28\u003c\/p\u003e \u003cp\u003e2.4.3 Power Modes 29\u003c\/p\u003e \u003cp\u003e2.4.4 Power Consumption 29\u003c\/p\u003e \u003cp\u003e2.4.5 Solid-State Drive (SDD) 29\u003c\/p\u003e \u003cp\u003e2.5 Fan 30\u003c\/p\u003e \u003cp\u003e2.6 Power Supply Unit 30\u003c\/p\u003e \u003cp\u003e2.7 Network Infrastructure 31\u003c\/p\u003e \u003cp\u003e2.7.1 Current Scenario 31\u003c\/p\u003e \u003cp\u003e2.7.2 New Energy-Oriented Model 32\u003c\/p\u003e \u003cp\u003e2.7.3 Current Advances in Networking 33\u003c\/p\u003e \u003cp\u003e2.7.4 Adaptive Link Rate (ALR) 34\u003c\/p\u003e \u003cp\u003e2.7.5 Low Power Idle (LPI) 34\u003c\/p\u003e \u003cp\u003e2.7.6 Energy-Aware Dynamic RWA Framework 34\u003c\/p\u003e \u003cp\u003e2.7.7 Energy-Aware Network Attacks 35\u003c\/p\u003e \u003cp\u003eReferences 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 GREEN WIRED NETWORKS 41\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAlfonso 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, \u003c\/i\u003e\u003ci\u003eLouis-Francois Pau, Chris Phillips, Sergio Ricciardi, Remi Sharrock, Patricia Stolf, Tuan Trinh, and Luca Valcarenghi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Economic Incentives and Green Tariffing 44\u003c\/p\u003e \u003cp\u003e3.1.1 Regulatory, Economic, and Microeconomic Measures 44\u003c\/p\u003e \u003cp\u003e3.1.2 Pricing Theory in Relation to Green Policies 46\u003c\/p\u003e \u003cp\u003e3.1.3 COST Action Results 50\u003c\/p\u003e \u003cp\u003e3.2 Network Components 51\u003c\/p\u003e \u003cp\u003e3.2.1 Router 51\u003c\/p\u003e \u003cp\u003e3.2.2 Network Interface Card 55\u003c\/p\u003e \u003cp\u003e3.2.3 Reconfigurable Optical Add-Drop Multiplexer 56\u003c\/p\u003e \u003cp\u003e3.2.4 Digital Subscriber Line Access Multiplexer 56\u003c\/p\u003e \u003cp\u003e3.3 Architectures 57\u003c\/p\u003e \u003cp\u003e3.3.1 Access Networks 57\u003c\/p\u003e \u003cp\u003e3.3.2 Carrier Networks 58\u003c\/p\u003e \u003cp\u003e3.3.3 Grid Overlay Networks 58\u003c\/p\u003e \u003cp\u003e3.4 Traffic Considerations 59\u003c\/p\u003e \u003cp\u003e3.5 Energy-Saving Mechanisms 60\u003c\/p\u003e \u003cp\u003e3.5.1 Static Mechanisms 60\u003c\/p\u003e \u003cp\u003e3.5.2 Dynamic Mechanisms 61\u003c\/p\u003e \u003cp\u003e3.6 Challenges 72\u003c\/p\u003e \u003cp\u003e3.7 Summary 72\u003c\/p\u003e \u003cp\u003eReferences 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 GREEN WIRELESS-ENERGY EFFICIENCY IN WIRELESS NETWORKS 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVitor 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 Zinner\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 81\u003c\/p\u003e \u003cp\u003e4.2 Metrics and Trade-Offs in Wireless Networks 83\u003c\/p\u003e \u003cp\u003e4.2.1 Metrics 83\u003c\/p\u003e \u003cp\u003e4.2.2 Energy Optimization Trade-Offs 84\u003c\/p\u003e \u003cp\u003e4.2.3 Summary 85\u003c\/p\u003e \u003cp\u003e4.3 Measurement Methodology 85\u003c\/p\u003e \u003cp\u003e4.3.1 Energy Measurement Testbeds 86\u003c\/p\u003e \u003cp\u003e4.3.2 Energy Estimation Techniques 90\u003c\/p\u003e \u003cp\u003e4.3.3 Energy Measurements versus Estimation 97\u003c\/p\u003e \u003cp\u003e4.3.4 Summary 99\u003c\/p\u003e \u003cp\u003e4.4 Energy Efficiency and QoE in Wireless Access Networks 100\u003c\/p\u003e \u003cp\u003e4.4.1 Energy Issues in Cellular Networks 100\u003c\/p\u003e \u003cp\u003e4.4.2 Energy Efficiency and QoE in Wireless Mesh Networks 101\u003c\/p\u003e \u003cp\u003e4.4.3 Reducing Energy Consumption of the End User Device 105\u003c\/p\u003e \u003cp\u003e4.4.4 Energy Measurements Revealing Video QoE Issues 108\u003c\/p\u003e \u003cp\u003e4.4.5 Energy Issues in Environmental WMNs 110\u003c\/p\u003e \u003cp\u003e4.4.6 Summary 112\u003c\/p\u003e \u003cp\u003e4.5 Energy-Efficient Medium Access in Wireless Sensor Networks 113\u003c\/p\u003e \u003cp\u003e4.5.1 MaxMAC – An Energy-Efficient MAC Protocol 113\u003c\/p\u003e \u003cp\u003e4.5.2 Real-World Testbed Experiments with MaxMAC 116\u003c\/p\u003e \u003cp\u003e4.5.3 Summary 119\u003c\/p\u003e \u003cp\u003e4.6 Energy-Efficient Connectivity in Ad-Hoc and Opportunistic Networks 119\u003c\/p\u003e \u003cp\u003e4.6.1 Ad-Hoc Networking 120\u003c\/p\u003e \u003cp\u003e4.6.2 Opportunistic and Delay-Tolerant Networking 121\u003c\/p\u003e \u003cp\u003e4.6.3 Summary 123\u003c\/p\u003e \u003cp\u003e4.7 Summary and Conclusions 124\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 POWER MODELING 131\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJason Mair, Zhiyi Huang, David Eyers, Leandro Cupertino, Georges Da Costa, Jean-Marc Pierson, and Helmut Hlavacs\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 131\u003c\/p\u003e \u003cp\u003e5.2 Measuring Power 133\u003c\/p\u003e \u003cp\u003e5.2.1 External Power Meters 133\u003c\/p\u003e \u003cp\u003e5.2.2 Internal Power Meters 134\u003c\/p\u003e \u003cp\u003e5.3 Performance Indicators 135\u003c\/p\u003e \u003cp\u003e5.3.1 Source Instrumentation 135\u003c\/p\u003e \u003cp\u003e5.3.2 Binary Instrumentation 136\u003c\/p\u003e \u003cp\u003e5.3.3 Performance Monitoring Counters 136\u003c\/p\u003e \u003cp\u003e5.3.4 Operating System Events 137\u003c\/p\u003e \u003cp\u003e5.3.5 Virtual Machine Performance 138\u003c\/p\u003e \u003cp\u003e5.4 Interaction between Power and Performance 138\u003c\/p\u003e \u003cp\u003e5.4.1 Central Processing Unit (CPU) 138\u003c\/p\u003e \u003cp\u003e5.4.2 Memory 140\u003c\/p\u003e \u003cp\u003e5.4.3 Input\/Output (I\/O) 141\u003c\/p\u003e \u003cp\u003e5.4.4 Network 141\u003c\/p\u003e \u003cp\u003e5.4.5 Idle States 142\u003c\/p\u003e \u003cp\u003e5.5 Power Modeling Procedure 143\u003c\/p\u003e \u003cp\u003e5.5.1 Variable Selection 143\u003c\/p\u003e \u003cp\u003e5.5.2 Training Data Collection 144\u003c\/p\u003e \u003cp\u003e5.5.3 Learning from Data 145\u003c\/p\u003e \u003cp\u003e5.5.4 Event Correlation 145\u003c\/p\u003e \u003cp\u003e5.5.5 Model Evaluation Concepts 146\u003c\/p\u003e \u003cp\u003e5.5.6 Power Estimation Errors 148\u003c\/p\u003e \u003cp\u003e5.5.7 Related Work 149\u003c\/p\u003e \u003cp\u003e5.6 Use-Cases 151\u003c\/p\u003e \u003cp\u003e5.6.1 Applications 151\u003c\/p\u003e \u003cp\u003e5.6.2 Single-Core Systems 152\u003c\/p\u003e \u003cp\u003e5.6.3 Multi-core and Multiprocessor 152\u003c\/p\u003e \u003cp\u003e5.6.4 Distributed Systems 153\u003c\/p\u003e \u003cp\u003e5.7 Available Software 154\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 155\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 GREEN DATA CENTERS 159\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRobert 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 Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 160\u003c\/p\u003e \u003cp\u003e6.2 Overview of Energy Consumption of Hardware Infrastructure in Data Center 161\u003c\/p\u003e \u003cp\u003e6.2.1 Energy Consumption Rankings and Metrics 161\u003c\/p\u003e \u003cp\u003e6.2.2 Processing: CPU, GPU, and memory 162\u003c\/p\u003e \u003cp\u003e6.2.3 Storage 168\u003c\/p\u003e \u003cp\u003e6.2.4 Communicating Elements 168\u003c\/p\u003e \u003cp\u003e6.3 Middleware Solutions that Regulate and Optimize the Energy Consumption in Data Centers 169\u003c\/p\u003e \u003cp\u003e6.3.1 An Overview of the Middleware 169\u003c\/p\u003e \u003cp\u003e6.3.2 System Modeling 171\u003c\/p\u003e \u003cp\u003e6.3.3 Control Mechanisms 172\u003c\/p\u003e \u003cp\u003e6.3.4 A Use Case of Leveraging Energy Efficiency in Data Centers 174\u003c\/p\u003e \u003cp\u003e6.4 Data Center Network Architectures 177\u003c\/p\u003e \u003cp\u003e6.4.1 Architectures 177\u003c\/p\u003e \u003cp\u003e6.4.2 Power Consumption of Data Center Architectures 181\u003c\/p\u003e \u003cp\u003e6.4.3 Additional Proposals for Energy-Efficient Data Centers 182\u003c\/p\u003e \u003cp\u003e6.5 Solutions for Cooling and Heat Control in Data Center 184\u003c\/p\u003e \u003cp\u003e6.5.1 Mechanical-Based Approaches 185\u003c\/p\u003e \u003cp\u003e6.5.2 Software-Based Approaches 187\u003c\/p\u003e \u003cp\u003eAcknowledgments 187\u003c\/p\u003e \u003cp\u003eReferences 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 ENERGY EFFICIENCY AND HIGH-PERFORMANCE COMPUTING 197\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePascal Bouvry, Ghislain Landry Tsafack Chetsa, Georges Da Costa, Emmanuel Jeannot, Laurent Lefèvre, Jean-Marc Pierson, Frédéric Pinel, Patricia Stolf, and Sébastien Varrette\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 197\u003c\/p\u003e \u003cp\u003e7.2 Overview of HPC Components and Latest Trends Toward Energy Efficiency 198\u003c\/p\u003e \u003cp\u003e7.2.1 Architecture of the Current HPC Facilities 198\u003c\/p\u003e \u003cp\u003e7.2.2 Overview of the Main HPC Components 201\u003c\/p\u003e \u003cp\u003e7.2.3 HPC Performance and Energy Efficiency Evaluation 203\u003c\/p\u003e \u003cp\u003e7.3 Building the Path to Exascale Computing 206\u003c\/p\u003e \u003cp\u003e7.3.1 The Exascale Challenge: Hardware and Architecture Issues 206\u003c\/p\u003e \u003cp\u003e7.3.2 Energy Efficiency and Resource and Job Management System (RJMS) 207\u003c\/p\u003e \u003cp\u003e7.3.3 Energy-Aware Software 210\u003c\/p\u003e \u003cp\u003e7.3.4 A Methodology for Energy Reduction in HPC 210\u003c\/p\u003e \u003cp\u003e7.4 Energy Efficiency of Virtualization and Cloud Frameworks over HPC Workloads 216\u003c\/p\u003e \u003cp\u003e7.5 Conclusion: Open Challenges 221\u003c\/p\u003e \u003cp\u003eAcknowledgments 222\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 SCHEDULING AND RESOURCE ALLOCATION 225\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePragati Agrawal, Damien Borgetto, Carmela Comito, Georges Da Costa, Jean-Marc Pierson, Payal Prakash, Shrisha Rao, Domenico Talia, Cheikhou Thiam, and Paolo Trunfio\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction: Energy-Aware Scheduling 225\u003c\/p\u003e \u003cp\u003e8.2 Use of Linear Programming in Energy-Aware Scheduling 226\u003c\/p\u003e \u003cp\u003e8.2.1 Finding the Optimal Solution Using a Linear Program 226\u003c\/p\u003e \u003cp\u003e8.2.2 Benefits and Limitations of LP 227\u003c\/p\u003e \u003cp\u003e8.3 Heuristics in Large Instances 228\u003c\/p\u003e \u003cp\u003e8.3.1 Energy-Aware Greedy Algorithms 229\u003c\/p\u003e \u003cp\u003e8.3.2 Vector Packing 229\u003c\/p\u003e \u003cp\u003e8.3.3 Improving Fast Algorithms 229\u003c\/p\u003e \u003cp\u003e8.4 Comparing Allocation Heuristics for Energy-Aware Scheduling 230\u003c\/p\u003e \u003cp\u003e8.4.1 Problem Formulation 230\u003c\/p\u003e \u003cp\u003e8.4.2 Allocation Heuristics 232\u003c\/p\u003e \u003cp\u003e8.4.3 Results 234\u003c\/p\u003e \u003cp\u003e8.5 Energy-Aware Task Allocation in Mobile Environments 236\u003c\/p\u003e \u003cp\u003e8.5.1 Reference Architecture 237\u003c\/p\u003e \u003cp\u003e8.5.2 Task Allocation Strategy 238\u003c\/p\u003e \u003cp\u003e8.5.3 Task Allocation Algorithm 239\u003c\/p\u003e \u003cp\u003e8.5.4 Performance Results 241\u003c\/p\u003e \u003cp\u003e8.6 An Energy-Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 243\u003c\/p\u003e \u003cp\u003e8.6.1 Decentralized Resources in Cloud: Overview 243\u003c\/p\u003e \u003cp\u003e8.6.2 Cooperative Scheduling Anti-Load Balancing Algorithm for Cloud (CSAAC) 244\u003c\/p\u003e \u003cp\u003e8.6.3 Simulation Results 245\u003c\/p\u003e \u003cp\u003e8.6.4 Evaluation 248\u003c\/p\u003e \u003cp\u003e8.7 Cost-Aware Scheduling with Smart Grids 248\u003c\/p\u003e \u003cp\u003e8.7.1 Cost-Aware Scheduling 248\u003c\/p\u003e \u003cp\u003e8.7.2 Cost-Aware Scheduling Using DE 252\u003c\/p\u003e \u003cp\u003e8.7.3 Comparison of DE with Other Approaches 254\u003c\/p\u003e \u003cp\u003e8.8 Heterogeneity, Cooling, DVFS, and Migration 257\u003c\/p\u003e \u003cp\u003e8.8.1 Lever Interactions 257\u003c\/p\u003e \u003cp\u003e8.8.2 Infrastructures 257\u003c\/p\u003e \u003cp\u003e8.8.3 Resource Allocation as a Whole 258\u003c\/p\u003e \u003cp\u003e8.9 Conclusions 259\u003c\/p\u003e \u003cp\u003eReferences 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 ENERGY EFFICIENCY IN P2P SYSTEMS AND APPLICATIONS 263\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSimone Brienza, Sena Efsun Cebeci, Seyed-Saeid Masoumzadeh, Helmut Hlavacs, Öznur Özkasap, Giuseppe Anastasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 264\u003c\/p\u003e \u003cp\u003e9.2 General Approaches to Energy Efficiency 264\u003c\/p\u003e \u003cp\u003e9.2.1 Sleep\/Wakeup Approaches 264\u003c\/p\u003e \u003cp\u003e9.2.2 Hierarchical Approaches 266\u003c\/p\u003e \u003cp\u003e9.2.3 Resource Allocation 268\u003c\/p\u003e \u003cp\u003e9.3 Energy Efficiency in File-Sharing Applications 269\u003c\/p\u003e \u003cp\u003e9.3.1 Client–Server versus P2P File Sharing 269\u003c\/p\u003e \u003cp\u003e9.3.2 Energy Efficiency in P2P File Sharing 270\u003c\/p\u003e \u003cp\u003e9.3.3 Energy Efficiency in BitTorrent 270\u003c\/p\u003e \u003cp\u003e9.3.4 Energy Efficiency in Other File-Sharing Protocols 279\u003c\/p\u003e \u003cp\u003e9.4 Energy Efficiency in P2P Epidemic Protocols 280\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 282\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 TOWARD SUSTAINABILITY FOR LARGE-SCALE COMPUTING SYSTEMS: ENVIRONMENTAL, ECONOMIC, AND STANDARDIZATION ASPECTS 287\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChristina Herzog, Jean-Marc Pierson, and Laurent Lefèvre\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 287\u003c\/p\u003e \u003cp\u003e10.2 Green IT for Innovation and Innovation for Green IT 288\u003c\/p\u003e \u003cp\u003e10.2.1 Defining Green IT and Its Link with Sustainability 288\u003c\/p\u003e \u003cp\u003e10.2.2 Differences between Academia and Companies 291\u003c\/p\u003e \u003cp\u003e10.2.3 Describing the Loop between Academia and Industry 294\u003c\/p\u003e \u003cp\u003e10.3 Standardization Landscape in Green IT 295\u003c\/p\u003e \u003cp\u003e10.3.1 Different Standardization Levels 296\u003c\/p\u003e \u003cp\u003e10.3.2 Standardization Bodies 297\u003c\/p\u003e \u003cp\u003e10.3.3 Regulations 299\u003c\/p\u003e \u003cp\u003e10.3.4 Industry Groups and Professional Bodies 299\u003c\/p\u003e \u003cp\u003e10.3.5 Analysis of the Standardization Actors 301\u003c\/p\u003e \u003cp\u003e10.4 Modeling Actors of Innovation in Green IT and their Links 301\u003c\/p\u003e \u003cp\u003e10.4.1 Researcher 301\u003c\/p\u003e \u003cp\u003e10.4.2 Universities 302\u003c\/p\u003e \u003cp\u003e10.4.3 Technology Transfer Office (TTO) 302\u003c\/p\u003e \u003cp\u003e10.4.4 Industry 302\u003c\/p\u003e \u003cp\u003e10.4.5 Funding Organization 303\u003c\/p\u003e \u003cp\u003e10.4.6 Standardization Body 303\u003c\/p\u003e \u003cp\u003e10.4.7 Links between Actors 303\u003c\/p\u003e \u003cp\u003e10.4.8 Rating the Relationships between Actors 304\u003c\/p\u003e \u003cp\u003e10.5 Using the Modeling for Deciding 306\u003c\/p\u003e \u003cp\u003e10.5.1 Methodology to be Developed 306\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 307\u003c\/p\u003e \u003cp\u003eAcknowledgment 307\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003eAuthor Index 309\u003c\/p\u003e \u003cp\u003eSubject Index 311\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eJean-Marc Pierson\u003c\/strong\u003e 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAddresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. \u003ci\u003eLarge-Scale Distributed Systems and Energy Efficiency: A Holistic View\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDiscusses measurement and modeling of the energy consumption method\u003c\/li\u003e \u003cli\u003eIncludes methods for energy consumption reduction in diverse computing environments\u003c\/li\u003e \u003cli\u003eFeatures a variety of case studies and examples of energy reduction and assessment\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTimely and important, \u003ci\u003eLarge-Scale Distributed Systems and Energy Efficiency \u003c\/i\u003eis an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eJean-Marc Pierson\u003c\/b\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989510013157,"sku":"NP9781118864630","price":123.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118864630.jpg?v=1761784396","url":"https:\/\/k12savings.com\/es\/products\/large-scale-distributed-systems-and-energy-efficiency-isbn-9781118864630","provider":"K12savings","version":"1.0","type":"link"}