Large-Scale Distributed Systems and Energy Efficiency: A Holistic View

ISBN-13: 9781118864630 / Angielski / Twarda / 2015 / 336 str.

Jean-Marc Pierson

Opis

Addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks With 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 method
• Includes methods for energy consumption reduction in diverse computing environments
• Features a variety of case studies and examples of energy reduction and assessment

Timely 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.

Spis treści

Preface xv

Acknowledgment xvii

1 INTRODUCTION TO ENERGY EFFICIENCY IN LARGE–SCALE DISTRIBUTED SYSTEMS 1
Jean–Marc Pierson and Helmut Hlavacs

1.1 Energy Consumption Status 1

1.2 Target of the Book 3

1.3 The Cost Action IC0804 4

1.3.1 Birth of the Action 4

1.3.2 Development of the Action 5

1.3.3 End and Future of the Action 10

1.4 Chapters Preview 11

Acknowledgement 12

References 12

2 HARDWARE LEVERAGES FOR ENERGY REDUCTION IN LARGE–SCALE DISTRIBUTED SYSTEMS 17
Davide Careglio, Georges Da Costa, and Sergio Ricciardi

2.1 Introduction 17

2.1.1 Motivation for Energy–Aware Distributed Computing 17

2.2 Processor 19

2.2.1 Context 19

2.2.2 Advanced Configuration and Power Interface (ACPI) 20

2.2.3 Vendors 21

2.2.4 General–Purpose Graphics Processing Unit (GPGPU) 23

2.2.5 ARM Architecture 24

2.3 Memory (DRAM) 25

2.3.1 Context 25

2.3.2 Power Consumption 25

2.3.3 Energy Efficiency Techniques 26

2.3.4 Vendors 26

2.4 Disk/Flash 27

2.4.1 Spindle Speed 28

2.4.2 Seek Speed 28

2.4.3 Power Modes 29

2.4.4 Power Consumption 29

2.4.5 Solid–State Drive (SDD) 29

2.5 Fan 30

2.6 Power Supply Unit 30

2.7 Network Infrastructure 31

2.7.1 Current Scenario 31

2.7.2 New Energy–Oriented Model 32

2.7.3 Current Advances in Networking 33

2.7.4 Adaptive Link Rate (ALR) 34

2.7.5 Low Power Idle (LPI) 34

2.7.6 Energy–Aware Dynamic RWA Framework 34

2.7.7 Energy–Aware Network Attacks 35

References 36

3 GREEN WIRED NETWORKS 41
Alfonso 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 Valcarenghi

3.1 Economic Incentives and Green Tariffing 44

3.1.1 Regulatory, Economic, and Microeconomic Measures 44

3.1.2 Pricing Theory in Relation to Green Policies 46

3.1.3 COST Action Results 50

3.2 Network Components 51

3.2.1 Router 51

3.2.2 Network Interface Card 55

3.2.3 Reconfigurable Optical Add–Drop Multiplexer 56

3.2.4 Digital Subscriber Line Access Multiplexer 56

3.3 Architectures 57

3.3.1 Access Networks 57

3.3.2 Carrier Networks 58

3.3.3 Grid Overlay Networks 58

3.4 Traffic Considerations 59

3.5 Energy–Saving Mechanisms 60

3.5.1 Static Mechanisms 60

3.5.2 Dynamic Mechanisms 61

3.6 Challenges 72

3.7 Summary 72

References 73

4 GREEN WIRELESS–ENERGY EFFICIENCY IN WIRELESS NETWORKS 81
Vitor 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

4.1 Introduction 81

4.2 Metrics and Trade–Offs in Wireless Networks 83

4.2.1 Metrics 83

4.2.2 Energy Optimization Trade–Offs 84

4.2.3 Summary 85

4.3 Measurement Methodology 85

4.3.1 Energy Measurement Testbeds 86

4.3.2 Energy Estimation Techniques 90

4.3.3 Energy Measurements versus Estimation 97

4.3.4 Summary 99

4.4 Energy Efficiency and QoE in Wireless Access Networks 100

4.4.1 Energy Issues in Cellular Networks 100

4.4.2 Energy Efficiency and QoE in Wireless Mesh Networks 101

4.4.3 Reducing Energy Consumption of the End User Device 105

4.4.4 Energy Measurements Revealing Video QoE Issues 108

4.4.5 Energy Issues in Environmental WMNs 110

4.4.6 Summary 112

4.5 Energy–Efficient Medium Access in Wireless Sensor Networks 113

4.5.1 MaxMAC An Energy–Efficient MAC Protocol 113

4.5.2 Real–World Testbed Experiments with MaxMAC 116

4.5.3 Summary 119

4.6 Energy–Efficient Connectivity in Ad–Hoc and Opportunistic Networks 119

4.6.1 Ad–Hoc Networking 120

4.6.2 Opportunistic and Delay–Tolerant Networking 121

4.6.3 Summary 123

4.7 Summary and Conclusions 124

References 125

5 POWER MODELING 131
Jason Mair, Zhiyi Huang, David Eyers, Leandro Cupertino, Georges Da Costa, Jean–Marc Pierson, and Helmut Hlavacs

5.1 Introduction 131

5.2 Measuring Power 133

5.2.1 External Power Meters 133

5.2.2 Internal Power Meters 134

5.3 Performance Indicators 135

5.3.1 Source Instrumentation 135

5.3.2 Binary Instrumentation 136

5.3.3 Performance Monitoring Counters 136

5.3.4 Operating System Events 137

5.3.5 Virtual Machine Performance 138

5.4 Interaction between Power and Performance 138

5.4.1 Central Processing Unit (CPU) 138

5.4.2 Memory 140

5.4.3 Input/Output (I/O) 141

5.4.4 Network 141

5.4.5 Idle States 142

5.5 Power Modeling Procedure 143

5.5.1 Variable Selection 143

5.5.2 Training Data Collection 144

5.5.3 Learning from Data 145

5.5.4 Event Correlation 145

5.5.5 Model Evaluation Concepts 146

5.5.6 Power Estimation Errors 148

5.5.7 Related Work 149

5.6 Use–Cases 151

5.6.1 Applications 151

5.6.2 Single–Core Systems 152

5.6.3 Multi–core and Multiprocessor 152

5.6.4 Distributed Systems 153

5.7 Available Software 154

5.8 Conclusion 155

References 156

6 GREEN DATA CENTERS 159
Robert 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

6.1 Introduction 160

6.2 Overview of Energy Consumption of Hardware Infrastructure in Data Center 161

6.2.1 Energy Consumption Rankings and Metrics 161

6.2.2 Processing: CPU, GPU, and memory 162

6.2.3 Storage 168

6.2.4 Communicating Elements 168

6.3 Middleware Solutions that Regulate and Optimize the Energy Consumption in Data Centers 169

6.3.1 An Overview of the Middleware 169

6.3.2 System Modeling 171

6.3.3 Control Mechanisms 172

6.3.4 A Use Case of Leveraging Energy Efficiency in Data Centers 174

6.4 Data Center Network Architectures 177

6.4.1 Architectures 177

6.4.2 Power Consumption of Data Center Architectures 181

6.4.3 Additional Proposals for Energy–Efficient Data Centers 182

6.5 Solutions for Cooling and Heat Control in Data Center 184

6.5.1 Mechanical–Based Approaches 185

6.5.2 Software–Based Approaches 187

Acknowledgments 187

References 188

7 ENERGY EFFICIENCY AND HIGH–PERFORMANCE COMPUTING 197
Pascal Bouvry, Ghislain Landry Tsafack Chetsa, Georges Da Costa, Emmanuel Jeannot, Laurent Lefèvre, Jean–Marc Pierson, Frédéric Pinel, Patricia Stolf, and Sébastien Varrette

7.1 Introduction 197

7.2 Overview of HPC Components and Latest Trends Toward Energy Efficiency 198

7.2.1 Architecture of the Current HPC Facilities 198

7.2.2 Overview of the Main HPC Components 201

7.2.3 HPC Performance and Energy Efficiency Evaluation 203

7.3 Building the Path to Exascale Computing 206

7.3.1 The Exascale Challenge: Hardware and Architecture Issues 206

7.3.2 Energy Efficiency and Resource and Job Management System (RJMS) 207

7.3.3 Energy–Aware Software 210

7.3.4 A Methodology for Energy Reduction in HPC 210

7.4 Energy Efficiency of Virtualization and Cloud Frameworks over HPC Workloads 216

7.5 Conclusion: Open Challenges 221

Acknowledgments 222

References 222

8 SCHEDULING AND RESOURCE ALLOCATION 225
Pragati Agrawal, Damien Borgetto, Carmela Comito, Georges Da Costa, Jean–Marc Pierson, Payal Prakash, Shrisha Rao, Domenico Talia, Cheikhou Thiam, and Paolo Trunfio

8.1 Introduction: Energy–Aware Scheduling 225

8.2 Use of Linear Programming in Energy–Aware Scheduling 226

8.2.1 Finding the Optimal Solution Using a Linear Program 226

8.2.2 Benefits and Limitations of LP 227

8.3 Heuristics in Large Instances 228

8.3.1 Energy–Aware Greedy Algorithms 229

8.3.2 Vector Packing 229

8.3.3 Improving Fast Algorithms 229

8.4 Comparing Allocation Heuristics for Energy–Aware Scheduling 230

8.4.1 Problem Formulation 230

8.4.2 Allocation Heuristics 232

8.4.3 Results 234

8.5 Energy–Aware Task Allocation in Mobile Environments 236

8.5.1 Reference Architecture 237

8.5.2 Task Allocation Strategy 238

8.5.3 Task Allocation Algorithm 239

8.5.4 Performance Results 241

8.6 An Energy–Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 243

8.6.1 Decentralized Resources in Cloud: Overview 243

8.6.2 Cooperative Scheduling Anti–Load Balancing Algorithm for Cloud (CSAAC) 244

8.6.3 Simulation Results 245

8.6.4 Evaluation 248

8.7 Cost–Aware Scheduling with Smart Grids 248

8.7.1 Cost–Aware Scheduling 248

8.7.2 Cost–Aware Scheduling Using DE 252

8.7.3 Comparison of DE with Other Approaches 254

8.8 Heterogeneity, Cooling, DVFS, and Migration 257

8.8.1 Lever Interactions 257

8.8.2 Infrastructures 257

8.8.3 Resource Allocation as a Whole 258

8.9 Conclusions 259

References 260

9 ENERGY EFFICIENCY IN P2P SYSTEMS AND APPLICATIONS 263
Simone Brienza, Sena Efsun Cebeci, Seyed–Saeid Masoumzadeh, Helmut Hlavacs, Öznur Özkasap, Giuseppe Anastasi

9.1 Introduction 264

9.2 General Approaches to Energy Efficiency 264

9.2.1 Sleep/Wakeup Approaches 264

9.2.2 Hierarchical Approaches 266

9.2.3 Resource Allocation 268

9.3 Energy Efficiency in File–Sharing Applications 269

9.3.1 Client Server versus P2P File Sharing 269

9.3.2 Energy Efficiency in P2P File Sharing 270

9.3.3 Energy Efficiency in BitTorrent 270

9.3.4 Energy Efficiency in Other File–Sharing Protocols 279

9.4 Energy Efficiency in P2P Epidemic Protocols 280

9.5 Conclusions 282

References 283

10 TOWARD SUSTAINABILITY FOR LARGE–SCALE COMPUTING SYSTEMS: ENVIRONMENTAL, ECONOMIC, AND STANDARDIZATION ASPECTS 287
Christina Herzog, Jean–Marc Pierson, and Laurent Lefèvre

10.1 Introduction 287

10.2 Green IT for Innovation and Innovation for Green IT 288

10.2.1 Defining Green IT and Its Link with Sustainability 288

10.2.2 Differences between Academia and Companies 291

10.2.3 Describing the Loop between Academia and Industry 294

10.3 Standardization Landscape in Green IT 295

10.3.1 Different Standardization Levels 296

10.3.2 Standardization Bodies 297

10.3.3 Regulations 299

10.3.4 Industry Groups and Professional Bodies 299

10.3.5 Analysis of the Standardization Actors 301

10.4 Modeling Actors of Innovation in Green IT and their Links 301

10.4.1 Researcher 301

10.4.2 Universities 302

10.4.3 Technology Transfer Office (TTO) 302

10.4.4 Industry 302

10.4.5 Funding Organization 303

10.4.6 Standardization Body 303

10.4.7 Links between Actors 303

10.4.8 Rating the Relationships between Actors 304

10.5 Using the Modeling for Deciding 306

10.5.1 Methodology to be Developed 306

10.6 Conclusion 307

Acknowledgment 307

References 307

Author Index 309

Subject Index 311

Blurb

Addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks

With 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 method
• Includes methods for energy consumption reduction in diverse computing environments
• Features a variety of case studies and examples of energy reduction and assessment

Timely 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.

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.