Jean-Marc Pierson

Large-scale Distributed Systems and Energy Efficiency (eBook, PDF)

A Holistic View

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 336
• Erscheinungstermin: 5. März 2015
• Englisch
• ISBN-13: 9781118959121
• Artikelnr.: 42513333

Autorenporträt

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.

Inhaltsangabe

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