PREFACE xxix
ACKNOWLEDGMENTS xxxi
CONTRIBUTORS xxxiii
1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSOR COMPUTERS WITH ENERGY AND TIME CONSTRAINTS 1
Keqin Li
1.1 Introduction 1
1.1.1 Energy Consumption 1
1.1.2 Power Reduction 2
1.1.3 Dynamic Power Management 3
1.1.4 Task Scheduling with Energy and Time Constraints 4
1.1.5 Chapter Outline 5
1.2 Preliminaries 5
1.2.1 Power Consumption Model 5
1.2.2 Problem Definitions 6
1.2.3 Task Models 7
1.2.4 Processor Models 8
1.2.5 Scheduling Models 9
1.2.6 Problem Decomposition 9
1.2.7 Types of Algorithms 10
1.3 Problem Analysis 10
1.3.1 Schedule Length Minimization 10
1.3.1.1 Uniprocessor computers 10
1.3.1.2 Multiprocessor computers 11
1.3.2 Energy Consumption Minimization 12
1.3.2.1 Uniprocessor computers 12
1.3.2.2 Multiprocessor computers 13
1.3.3 Strong NP-Hardness 14
1.3.4 Lower Bounds 14
1.3.5 Energy-Delay Trade-off 15
1.4 Pre-Power-Determination Algorithms 16
1.4.1 Overview 16
1.4.2 Performance Measures 17
1.4.3 Equal-Time Algorithms and Analysis 18
1.4.3.1 Schedule length minimization 18
1.4.3.2 Energy consumption minimization 19
1.4.4 Equal-Energy Algorithms and Analysis 19
1.4.4.1 Schedule length minimization 19
1.4.4.2 Energy consumption minimization 21
1.4.5 Equal-Speed Algorithms and Analysis 22
1.4.5.1 Schedule length minimization 22
1.4.5.2 Energy consumption minimization 23
1.4.6 Numerical Data 24
1.4.7 Simulation Results 25
1.5 Post-Power-Determination Algorithms 28
1.5.1 Overview 28
1.5.2 Analysis of List Scheduling Algorithms 29
1.5.2.1 Analysis of algorithm LS 29
1.5.2.2 Analysis of algorithm LRF 30
1.5.3 Application to Schedule Length Minimization 30
1.5.4 Application to Energy Consumption Minimization 31
1.5.5 Numerical Data 32
1.5.6 Simulation Results 32
1.6 Summary and Further Research 33
References 34
2 POWER-AWARE HIGH PERFORMANCE COMPUTING 39
Rong Ge and Kirk W. Cameron
2.1 Introduction 39
2.2 Background 41
2.2.1 Current Hardware Technology and Power Consumption 41
2.2.1.1 Processor power 41
2.2.1.2 Memory subsystem power 42
2.2.2 Performance 43
2.2.3 Energy Efficiency 44
2.3 Related Work 45
2.3.1 Power Profiling 45
2.3.1.1 Simulator-based power estimation 45
2.3.1.2 Direct measurements 46
2.3.1.3 Event-based estimation 46
2.3.2 Performance Scalability on Power-Aware Systems 46
2.3.3 Adaptive Power Allocation for Energy-Efficient Computing 47
2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications 48
2.4.1 Design and Implementation of PowerPack 48
2.4.1.1 Overview 48
2.4.1.2 Fine-grain systematic power measurement 50
2.4.1.3 Automatic power profiling and code synchronization 51
2.4.2 Power Profiles of HPC Applications and Systems 53
2.4.2.1 Power distribution over components 53
2.4.2.2 Power dynamics of applications 54
2.4.2.3 Power bounds on HPC systems 55
2.4.2.4 Power versus dynamic voltage and frequency scaling 57
2.5 Power-Aware Speedup Model 59
2.5.1 Power-Aware Speedup 59
2.5.1.1 Sequential execution time for a single workload T1(w, f ) 60
2.5.1.2 Sequential execution time for an ON-chip/OFF-chip workload 60
2.5.1.3 Parallel execution time on N processors for an ON-/OFF-chip workload with DOP = i 61
2.5.1.4 Power-aware speedup for DOP and ON-/OFF-chip workloads 62
2.5.2 Model Parametrization and Validation 63
2.5.2.1 Coarse-grain parametrization and validation 64
2.5.2.2 Fine-grain parametrization and validation 66
2.6 Model Usages 69
2.6.1 Identification of Optimal System Configurations 70
2.6.2 PAS-Directed Energy-Driven Runtime Frequency Scaling 71
2.7 Conclusion 73
References 75
3 ENERGY EFFICIENCY IN HPC SYSTEMS 81
Ivan Rodero and Manish Parashar
3.1 Introduction 81
3.2 Background and Related Work 83
3.2.1 CPU Power Management 83
3.2.1.1 OS-level CPU power management 83
3.2.1.2 Workload-level CPU power management 84
3.2.1.3 Cluster-level CPU power management 84
3.2.2 Component-Based Power Management 85
3.2.2.1 Memory subsystem 85
3.2.2.2 Storage subsystem 86
3.2.3 Thermal-Conscious Power Management 87
3.2.4 Power Management in Virtualized Datacenters 87
3.3 Proactive, Component-Based Power Management 88
3.3.1 Job Allocation Policies 88
3.3.2 Workload Profiling 90
3.4 Quantifying Energy Saving Possibilities 91
3.4.1 Methodology 92
3.4.2 Component-Level Power Requirements 92
3.4.3 Energy Savings 94
3.5 Evaluation of the Proposed Strategies 95
3.5.1 Methodology 96
3.5.2 Workloads 96
3.5.3 Metrics 97
3.6 Results 97
3.7 Concluding Remarks 102
3.8 Summary 103
References 104
4 A STOCHASTIC FRAMEWORK FOR HIERARCHICAL SYSTEM-LEVEL POWER MANAGEMENT 109
Peng Rong and Massoud Pedram
4.1 Introduction 109
4.2 Related Work 111
4.3 A Hierarchical DPM Architecture 113
4.4 Modeling 114
4.4.1 Model of the Application Pool 114
4.4.2 Model of the Service Flow Control 118
4.4.3 Model of the Simulated Service Provider 119
4.4.4 Modeling Dependencies between SPs 120
4.5 Policy Optimization 122
4.5.1 Mathematical Formulation 122
4.5.2 Optimal Time-Out Policy for Local Power Manager 123
4.6 Experimental Results 125
4.7 Conclusion 130
References 130
5 ENERGY-EFFICIENT RESERVATION INFRASTRUCTURE FOR GRIDS, CLOUDS, AND NETWORKS 133
Anne-Ce´ cile Orgerie and Laurent Lefe` vre
5.1 Introduction 133
5.2 Related Works 134
5.2.1 Server and Data Center Power Management 135
5.2.2 Node Optimizations 135
5.2.3 Virtualization to Improve Energy Efficiency 136
5.2.4 Energy Awareness in Wired Networking Equipment 136
5.2.5 Synthesis 137
5.3 ERIDIS: Energy-Efficient Reservation Infrastructure for Large-Scale Distributed Systems 138
5.3.1 ERIDIS Architecture 138
5.3.2 Management of the Resource Reservations 141
5.3.3 Resource Management and On/Off Algorithms 145
5.3.4 Energy-Consumption Estimates 146
5.3.5 Prediction Algorithms 146
5.4 EARI: Energy-Aware Reservation Infrastructure for Data Centers and Grids 147
5.4.1 EARIâs Architecture 147
5.4.2 Validation of EARI on Experimental Grid Traces 147
5.5 GOC: Green Open Cloud 149
5.5.1 GOCâs Resource Manager Architecture 150
5.5.2 Validation of the GOC Framework 152
5.6 HERMES: High Level Energy-Aware Model for Bandwidth Reservation in End-To-End Networks 152
5.6.1 HERMESâ Architecture 154
5.6.2 The Reservation Process of HERMES 155
5.6.3 Discussion 157
5.7 Summary 158
References 158
6 ENERGY-EFFICIENT JOB PLACEMENT ON CLUSTERS, GRIDS, AND CLOUDS 163
Damien Borgetto, Henri Casanova, Georges Da Costa, and Jean-Marc Pierson
6.1 Problem and Motivation 163
6.1.1 Context 163
6.1.2 Chapter Roadmap 164
6.2 Energy-Aware Infrastructures 164
6.2.1 Buildings 165
6.2.2 Context-Aware Buildings 165
6.2.3 Cooling 166
6.3 Current Resource Management Practices 167
6.3.1 Widely Used Resource Management Systems 167
6.3.2 Job Requirement Description 169
6.4 Scientific and Technical Challenges 170
6.4.1 Theoretical Difficulties 170
6.4.2 Technical Difficulties 170
6.4.3 Controlling and Tuning Jobs 171
6.5 Energy-Aware Job Placement Algorithms 172
6.5.1 State of the Art 172
6.5.2 Detailing One Approach 174
6.6 Discussion 180
6.6.1 Open Issues and Opportunities 180
6.6.2 Obstacles for Adoption in Production 182
6.7 Conclusion 183
References 184
7 COMPARISON AND ANALYSIS OF GREEDY ENERGY-EFFICIENT SCHEDULING ALGORITHMS FOR COMPUTATIONAL GRIDS 189
Peder Lindberg, James Leingang, Daniel Lysaker, Kashif Bilal, Samee Ullah Khan, Pascal Bouvry, Nasir Ghani, Nasro Min-Allah, and Juan Li
7.1 Introduction 189
7.2 Problem Formulation 191
7.2.1 The System Model 191
7.2.1.1 PEs 191
7.2.1.2 DVS 191
7.2.1.3 Tasks 192
7.2.1.4 Preliminaries 192
7.2.2 Formulating the Energy-Makespan Minimization Problem 192
7.3 Proposed Algorithms 193
7.3.1 Greedy Heuristics 194
7.3.1.1 Greedy heuristic scheduling algorithm 196
7.3.1.2 Greedy-min 197
7.3.1.3 Greedy-deadline 198
7.3.1.4 Greedy-max 198
7.3.1.5 MaxMin 199
7.3.1.6 ObFun 199
7.3.1.7 MinMin StdDev 202
7.3.1.8 MinMax StdDev 202
7.4 Simulations, Results, and Discussion 203
7.4.1 Workload 203
7.4.2 Comparative Results 204
7.4.2.1 Small-size problems 204
7.4.2.2 Large-size problems 206
7.5 Related Works 211
7.6 Conclusion 211
References 212
8 TOWARD ENERGY-AWARE SCHEDULING USING MACHINE LEARNING 215
Josep LL. Berral, InË igo Goiri, Ramon Nou, Ferran Julia` , Josep O. Fito´ , Jordi Guitart, Ricard Gavalda´ , and Jordi Torres
8.1 Introduction 215
8.1.1 Energetic Impact of the Cloud 216
8.1.2 An Intelligent Way to Manage Data Centers 216
8.1.3 Current Autonomic Computing Techniques 217
8.1.4 Power-Aware Autonomic Computing 217
8.1.5 State of the Art and Case Study 218
8.2 Intelligent Self-Management 218
8.2.1 Classical AI Approaches 219
8.2.1.1 Heuristic algorithms 219
8.2.1.2 AI planning 219
8.2.1.3 Semantic techniques 219
8.2.1.4 Expert systems and genetic algorithms 220
8.2.2 Machine Learning Approaches 220
8.2.2.1 Instance-based learning 221
8.2.2.2 Reinforcement learning 222
8.2.2.3 Feature and example selection 225
8.3 Introducing Power-Aware Approaches 225
8.3.1 Use of Virtualization 226
8.3.2 Turning On and Off Machines 228
8.3.3 Dynamic Voltage and Frequency Scaling 229
8.3.4 Hybrid Nodes and Data Centers 230
8.4 Experiences of Applying ML on Power-Aware Self-Management 230
8.4.1 Case Study Approach 231
8.4.2 Scheduling and Power Trade-Off 231
8.4.3 Experimenting with Power-Aware Techniques 233
8.4.4 Applying Machine Learning 236
8.4.5 Conclusions from the Experiments 238
8.5 Conclusions on Intelligent Power-Aware Self-Management 238
References 240
9 ENERGY EFFICIENCY METRICS FOR DATA CENTERS 245
Javid Taheri and Albert Y. Zomaya
9.1 Introduction 245
9.1.1 Background 245
9.1.2 Data Center Energy Use 246
9.1.3 Data Center Characteristics 246
9.1.3.1 Electric power 247
9.1.3.2 Heat removal 249
9.1.4 Energy Efficiency 250
9.2 Fundamentals of Metrics 250
9.2.1 Demand and Constraints on Data Center Operators 250
9.2.2 Metrics 251
9.2.2.1 Criteria for good metrics 251
9.2.2.2 Methodology 252
9.2.2.3 Stability of metrics 252
9.3 Data Center Energy Efficiency 252
9.3.1 Holistic IT Efficiency Metrics 252
9.3.1.1 Fixed versus proportional overheads 254
9.3.1.2 Power versus energy 254
9.3.1.3 Performance versus productivity 255
9.3.2 Code of Conduct 256
9.3.2.1 Environmental statement 256
9.3.2.2 Problem statement 256
9.3.2.3 Scope of the CoC 257
9.3.2.4 Aims and objectives of CoC 258
9.3.3 Power Use in Data Centers 259
9.3.3.1 Data center IT power to utility power relationship 259
9.3.3.2 Chiller efficiency and external temperature 260
9.4 Available Metrics 260
9.4.1 The Green Grid 261
9.4.1.1 Power usage effectiveness (PUE) 261
9.4.1.2 Data center efficiency (DCE) 262
9.4.1.3 Data center infrastructure efficiency (DCiE) 262
9.4.1.4 Data center productivity (DCP) 263
9.4.2 McKinsey 263
9.4.3 Uptime Institute 264
9.4.3.1 Site infrastructure power overhead multiplier (SI-POM) 265
9.4.3.2 IT hardware power overhead multiplier (H-POM) 266
9.4.3.3 DC hardware compute load per unit of computing work done 266
9.4.3.4 Deployed hardware utilization ratio (DH-UR) 266
9.4.3.5 Deployed hardware utilization efficiency (DH-UE) 267
9.5 Harmonizing Global Metrics for Data Center Energy Efficiency 267
References 268
10 AUTONOMIC GREEN COMPUTING IN LARGE-SCALE DATA CENTERS 271
Haoting Luo, Bithika Khargharia, Salim Hariri, and Youssif Al-Nashif
10.1 Introduction 271
10.2 Related Technologies and Techniques 272
10.2.1 Power Optimization Techniques in Data Centers 272
10.2.2 Design Model 273
10.2.3 Networks 274
10.2.4 Data Center Power Distribution 275
10.2.5 Data Center Power-Efficient Metrics 276
10.2.6 Modeling Prototype and Testbed 277
10.2.7 Green Computing 278
10.2.8 Energy Proportional Computing 280
10.2.9 Hardware Virtualization Technology 281
10.2.10 Autonomic Computing 282
10.3 Autonomic Green Computing: A Case Study 283
10.3.1 Autonomic Management Platform 285
10.3.1.1 Platform architecture 285
10.3.1.2 DEVS-based modeling and simulation platform 285
10.3.1.3 Workload generator 287
10.3.2 Model Parameter Evaluation 288
10.3.2.1 State transitioning overhead 288
10.3.2.2 VM template evaluation 289
10.3.2.3 Scalability analysis 291
10.3.3 Autonomic Power Efficiency Management Algorithm (Performance Per Watt) 291
10.3.4 Simulation Results and Evaluation 293
10.3.4.1 Analysis of energy and performance trade-offs 296
10.4 Conclusion and Future Directions 297
References 298
11 ENERGY AND THERMAL AWARE SCHEDULING IN DATA CENTERS 301
Gaurav Dhiman, Raid Ayoub, and Tajana S. Rosing
11.1 Introduction 301
11.2 Related Work 302
11.3 Intermachine Scheduling 305
11.3.1 Performance and Power Profile of VMs 305
11.3.2 Architecture 309
11.3.2.1 vgnode 309
11.3.2.2 vgxen 310
11.3.2.3 vgdom 312
11.3.2.4 vgserv 312
11.4 Intramachine Scheduling 315
11.4.1 Air-Forced Thermal Modeling and Cost 316
11.4.2 Cooling Aware Dynamic Workload Scheduling 317
11.4.3 Scheduling Mechanism 318
11.4.4 Cooling Costs Predictor 319
11.5 Evaluation 321
11.5.1 Intermachine Scheduler (vGreen) 321
11.5.2 Heterogeneous Workloads 323
11.5.2.1 Comparison with DVFS policies 325
11.5.2.2 Homogeneous workloads 328
11.5.3 Intramachine Scheduler (Cool and Save) 328
11.5.3.1 Results 331
11.5.3.2 Overhead of CAS 333
11.6 Conclusion 333
References 334
12 QOS-AWARE POWER MANAGEMENT IN DATA CENTERS 339
Jiayu Gong and Cheng-Zhong Xu
12.1 Introduction 339
12.2 Problem Classification 340
12.2.1 Objective and Constraint 340
12.2.2 Scope and Time Granularities 340
12.2.3 Methodology 341
12.2.4 Power Management Mechanism 342
12.3 Energy Efficiency 344
12.3.1 Energy-Efficiency Metrics 344
12.3.2 Improving Energy Efficiency 346
12.3.2.1 Energy minimization with performance guarantee 346
12.3.2.2 Performance maximization under power budget 348
12.3.2.3 Trade-off between power and performance 348
12.3.3 Energy-Proportional Computing 350
12.4 Power Capping 351
12.5 Conclusion 353
References 356
13 ENERGY-EFFICIENT STORAGE SYSTEMS FOR DATA CENTERS 361
Sudhanva Gurumurthi and Anand Sivasubramaniam
13.1 Introduction 361
13.2 Disk Drive Operation and Disk Power 362
13.2.1 An Overview of Disk Drives 362
13.2.2 Sources of Disk Power Consumption 363
13.2.3 Disk Activity and Power Consumption 365
13.3 Disk and Storage Power Reduction Techniques 366
13.3.1 Exploiting the STANDBY State 368
13.3.2 Reducing Seek Activity 369
13.3.3 Achieving Energy Proportionality 369
13.3.3.1 Hardware approaches 369
13.3.3.2 Software approaches 370
13.4 Using Nonvolatile Memory and Solid-State Disks 371
13.5 Conclusions 372
References 373
14 AUTONOMIC ENERGY/PERFORMANCE OPTIMIZATIONS FOR MEMORY IN SERVERS 377
Bithika Khargharia and Mazin Yousif
14.1 Introduction 378
14.2 Classifications of Dynamic Power Management Techniques 380
14.2.1 Heuristic and Predictive Techniques 380
14.2.2 QoS and Energy Trade-Offs 381
14.3 Applications of Dynamic Power Management (DPM) 382
14.3.1 Power Management of System Components in Isolation 382
14.3.2 Joint Power Management of System Components 383
14.3.3 Holistic System-Level Power Management 383
14.4 Autonomic Power and Performance Optimization of Memory Subsystems in Server Platforms 384
14.4.1 Adaptive Memory Interleaving Technique for Power and Performance Management 384
14.4.1.1 Formulating the optimization problem 386
14.4.1.2 Memory appflow 389
14.4.2 Industry Techniques 389
14.4.2.1 Enhancements in memory hardware design 390
14.4.2.2 Adding more operating states 390
14.4.2.3 Faster transition to and from low power states 390
14.4.2.4 Memory consolidation 390
14.5 Conclusion 391
References 391
15 ROD: A PRACTICAL APPROACH TO IMPROVING RELIABILITY OF ENERGY-EFFICIENT PARALLEL DISK SYSTEMS 395
Shu Yin, Xiaojun Ruan, Adam Manzanares, and Xiao Qin
15.1 Introduction 395
15.2 Modeling Reliability of Energy-Efficient Parallel Disks 396
15.2.1 The MINT Model 396
15.2.1.1 Disk utilization 398
15.2.1.2 Temperature 398
15.2.1.3 Power-state transition frequency 399
15.2.1.4 Single disk reliability model 399
15.2.2 MAID, Massive Arrays of Idle Disks 400
15.3 Improving Reliability of MAID via Disk Swapping 401
15.3.1 Improving Reliability of Cache Disks in MAID 401
15.3.2 Swapping Disks Multiple Times 404
15.4 Experimental Results and Evaluation 405
15.4.1 Experimental Setup 405
15.4.2 Disk Utilization 406
15.4.3 The Single Disk Swapping Strategy 406
15.4.4 The Multiple Disk Swapping Strategy 409
15.5 Related Work 411
15.6 Conclusions 412
References 413
16 EMBRACING THE MEMORY AND I/O WALLS FOR ENERGY-EFFICIENT SCIENTIFIC COMPUTING 417
Chung-Hsing Hsu and Wu-Chun Feng
16.1 Introduction 417
16.2 Background and Related Work 420
16.2.1 DVFS-Enabled Processors 420
16.2.2 DVFS Scheduling Algorithms 421
16.2.3 Memory-Aware, Interval-Based Algorithms 422
16.3 β-Adaptation: A New DVFS Algorithm 423
16.3.1 The Compute-Boundedness Metric, β 423
16.3.2 The Frequency Calculating Formula, f â- 424
16.3.3 The Online β Estimation 425
16.3.4 Putting It All Together 427
16.4 Algorithm Effectiveness 429
16.4.1 A Comparison to Other DVFS Algorithms 429
16.4.2 Frequency Emulation 432
16.4.3 The Minimum Dependence to the PMU 436
16.5 Conclusions and Future Work 438
References 439
17 MULTIPLE FREQUENCY SELECTION IN DVFS-ENABLED PROCESSORS TO MINIMIZE ENERGY CONSUMPTION 443
Nikzad Babaii Rizvandi, Albert Y. Zomaya, Young Choon Lee, Ali Javadzadeh Boloori, and Javid Taheri
17.1 Introduction 443
17.2 Energy Efficiency in HPC Systems 444
17.3 Exploitation of Dynamic Voltageâ"Frequency Scaling 446
17.3.1 Independent Slack Reclamation 446
17.3.2 Integrated Schedule Generation 447
17.4 Preliminaries 448
17.4.1 System and Application Models 448
17.4.2 Energy Model 448
17.5 Energy-Aware Scheduling via DVFS 450
17.5.1 Optimum Continuous Frequency 450
17.5.2 Reference Dynamic Voltageâ"Frequency Scaling (RDVFS) 451
17.5.3 Maximum-Minimum-Frequency for Dynamic Voltageâ"Frequency Scaling (MMF-DVFS) 452
17.5.4 Multiple Frequency Selection for Dynamic Voltageâ"Frequency Scaling (MFS-DVFS) 453
17.5.4.1 Task eligibility 454
17.6 Experimental Results 456
17.6.1 Simulation Settings 456
17.6.2 Results 458
17.7 Conclusion 461
References 461
18 THE PARAMOUNTCY OF RECONFIGURABLE COMPUTING 465
Reiner Hartenstein
18.1 Introduction 465
18.2 Why Computers are Important 466
18.2.1 Computing for a Sustainable Environment 470
18.3 Performance Progress Stalled 472
18.3.1 Unaffordable Energy Consumption of Computing 473
18.3.2 Crashing into the Programming Wall 475
18.4 The Tail is Wagging the Dog (Accelerators) 488
18.4.1 Hardwired Accelerators 489
18.4.2 Programmable Accelerators 490
18.5 Reconfigurable Computing 494
18.5.1 Speedup Factors by FPGAs 498
18.5.2 The Reconfigurable Computing Paradox 501
18.5.3 Saving Energy by Reconfigurable Computing 505
18.5.3.1 Traditional green computing 506
18.5.3.2 The role of graphics processors 507
18.5.3.3 Wintel versus ARM 508
18.5.4 Reconfigurable Computing is the Silver Bullet 511
18.5.4.1 A new world model of computing 511
18.5.5 The Twin-Paradigm Approach to Tear Down the Wall 514
18.5.6 A Mass Movement Needed as Soon as Possible 517
18.5.6.1 Legacy software from the mainframe age 518
18.5.7 How to Reinvent Computing 519 18.6 Conclusions 526
References 529
19 WORKLOAD CLUSTERING FOR INCREASING ENERGY SAVINGS ON EMBEDDED MPSOCS 549
Ozcan Ozturk, Mahmut Kandemir, and Sri Hari Krishna Narayanan
19.1 Introduction 549
19.2 Embedded MPSoC Architecture, Execution Model, and Related Work 550
19.3 Our Approach 551
19.3.1 Overview 551
19.3.2 Technical Details and Problem Formulation 553
19.3.2.1 System and job model 553
19.3.2.2 Mathematical programing model 554
19.3.2.3 Example 557
19.4 Experimental Evaluation 560
19.5 Conclusions 564
References 565
20 ENERGY-EFFICIENT INTERNET INFRASTRUCTURE 567
Weirong Jiang and Viktor K. Prasanna
20.1 Introduction 567
20.1.1 Performance Challenges 568
20.1.2 Existing Packet Forwarding Approaches 570
20.1.2.1 Software approaches 570
20.1.2.2 Hardware approaches 571
20.2 SRAM-Based Pipelined IP Lookup Architectures: Alternative to TCAMs 571
20.3 Data Structure Optimization for Power Efficiency 573
20.3.1 Problem Formulation 574
20.3.1.1 Non-pipelined and pipelined engines 574
20.3.1.2 Power function of SRAM 575
20.3.2 Special Case: Uniform Stride 576
20.3.3 Dynamic Programming 576
20.3.4 Performance Evaluation 577
20.3.4.1 Results for non-pipelined architecture 578
20.3.4.2 Results for pipelined architecture 578
20.4 Architectural Optimization to Reduce Dynamic Power Dissipation 580
20.4.1 Analysis and Motivation 581
20.4.1.1 Traffic locality 582
20.4.1.2 Traffic rate variation 582
20.4.1.3 Access frequency on different stages 583
20.4.2 Architecture-Specific Techniques 583
20.4.2.1 Inherent caching 584
20.4.2.2 Local clocking 584
20.4.2.3 Fine-grained memory enabling 585
20.4.3 Performance Evaluation 585
20.5 Related Work 588
20.6 Summary 589
References 589
21 DEMAND RESPONSE IN THE SMART GRID: A DISTRIBUTED COMPUTING PERSPECTIVE 593
Chen Wang and Martin De Groot
21.1 Introduction 593
21.2 Demand Response 595
21.2.1 Existing Demand Response Programs 595
21.2.2 Demand Response Supported by the Smart Grid 597
21.3 Demand Response as a Distributed System 600
21.3.1 An Overlay Network for Demand Response 600
21.3.2 Event Driven Demand Response 602
21.3.3 Cost Driven Demand Response 604
21.3.4 A Decentralized Demand Response Framework 609
21.3.5 Accountability of Coordination Decision Making 610
21.4 Summary 611
References 611
22 RESOURCE MANAGEMENT FOR DISTRIBUTED MOBILE COMPUTING 615
Jong-Kook Kim
22.1 Introduction 615
22.2 Single-Hop Energy-Constrained Environment 617
22.2.1 System Model 617
22.2.2 Related Work 620
22.2.3 Heuristic Descriptions 621
22.2.3.1 Mapping event 621
22.2.3.2 Scheduling communications 621
22.2.3.3 Opportunistic load balancing and minimum energy greedy heuristics 622
22.2.3.4 ME-MC heuristic 622
22.2.3.5 ME-ME heuristic 624
22.2.3.6 CRME heuristic 625
22.2.3.7 Originator and random 626
22.2.3.8 Upper bound 626
22.2.4 Simulation Model 628
22.2.5 Results 630
22.2.6 Summary 634
22.3 Multihop Distributed Mobile Computing Environment 635
22.3.1 The Multihop System Model 635
22.3.2 Energy-Aware Routing Protocol 636
22.3.2.1 Overview 636
22.3.2.2 DSDV 637
22.3.2.3 DSDV remaining energy 637
22.3.2.4 DSDV-energy consumption per remaining energy 637
22.3.3 Heuristic Description 638
22.3.3.1 Random 638
22.3.3.2 Estimated minimum total energy (EMTE) 638
22.3.3.3 K-percent-speed (KPS) and K-percent-energy (KPE) 639
22.3.3.4 Energy ratio and distance (ERD) 639
22.3.3.5 ETC and distance (ETCD) 640
22.3.3.6 Minimum execution time (MET) 640
22.3.3.7 Minimum completion time (MCT) and minimum completion time with DVS (MCT-DVS) 640
22.3.3.8 Switching algorithm (SA) 640
22.3.4 Simulation Model 641
22.3.5 Results 643
22.3.5.1 Distributed resource management 643
22.3.5.2 Energy-aware protocol 644
22.3.6 Summary 644
22.4 Future Work 647
References 647
23 AN ENERGY-AWARE FRAMEWORK FOR MOBILE DATA MINING 653
Carmela Comito, Domenico Talia, and Paolo Trunfio
23.1 Introduction 653
23.2 System Architecture 654
23.3 Mobile Device Components 657
23.4 Energy Model 659
23.5 Clustering Scheme 664
23.5.1 Clustering the M2M Architecture 666
23.6 Conclusion 670
References 670
24 ENERGY AWARENESS AND EFFICIENCY IN WIRELESS SENSOR NETWORKS: FROM PHYSICAL DEVICES TO THE COMMUNICATION LINK 673
Fla´ via C. Delicato and Paulo F. Pires
24.1 Introduction 673
24.2 WSN and Power Dissipation Models 676
24.2.1 Network and Node Architecture 676
24.2.2 Sources of Power Dissipation in WSNs 679
24.3 Strategies for Energy Optimization 683
24.3.1 Intranode Level 684
24.3.1.1 Duty cycling 685
24.3.1.2 Adaptive sensing 691
24.3.1.3 Dynamic voltage scale (DVS) 693
24.3.1.4 OS task scheduling 694
24.3.2 Internode Level 695
24.3.2.1 Transmission power control 695
24.3.2.2 Dynamic modulation scaling 696
24.3.2.3 Link layer optimizations 698
24.4 Final Remarks 701
References 702
25 NETWORK-WIDE STRATEGIES FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS 709
Fla´ via C. Delicato and Paulo F. Pires
25.1 Introduction 709
25.2 Data Link Layer 711
25.2.1 Topology Control Protocols 712
25.2.2 Energy-Efficient MAC Protocols 714
25.2.2.1 Scheduled MAC protocols in WSNs 716
25.2.2.2 Contention-based MAC protocols 717
25.3 Network Layer 719
25.3.1 Flat and Hierarchical Protocols 722
25.4 Transport Layer 725
25.5 Application Layer 729
25.5.1 Task Scheduling 729
25.5.2 Data Aggregation and Data Fusion in WSNs 733
25.5.2.1 Approaches of data fusion for energy efficiency 735
25.5.2.2 Data aggregation strategies 736
25.6 Final Remarks 740
References 741
26 ENERGY MANAGEMENT IN HETEROGENEOUS WIRELESS HEALTH CARE NETWORKS 751
Nima Nikzad, Priti Aghera, Piero Zappi, and Tajana S. Rosing
26.1 Introduction 751
26.2 System Model 753
26.2.1 Health Monitoring Task Model 753
26.3 Collaborative Distributed Environmental Sensing 755
26.3.1 Node Neighborhood and Localization Rate 757
26.3.2 Energy Ratio and Sensing Rate 758
26.3.3 Duty Cycling and Prediction 759
26.4 Task Assignment in a Body Area Network 760
26.4.1 Optimal Task Assignment 760
26.4.2 Dynamic Task Assignment 762
26.4.2.1 DynAGreen algorithm 763
26.4.2.2 DynAGreenLife algorithm 768
26.5 Results 771
26.5.1 Collaborative Sensing 771
26.5.1.1 Results 772
26.5.2 Dynamic Task Assignment 776
26.5.2.1 Performance in static conditions 777
26.5.2.2 Dynamic adaptability 780
26.6 Conclusion 784
References 785
INDEX 787
