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 EARI’s Architecture 147

5.4.2 Validation of EARI on Experimental Grid Traces 147

5.5 GOC: Green Open Cloud 149

5.5.1 GOC’s 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 HERMES’ 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, In˜ 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 ∗ 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 Voltage–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 Voltage–Frequency Scaling (RDVFS) 451

17.5.3 Maximum-Minimum-Frequency for Dynamic Voltage–Frequency Scaling (MMF-DVFS) 452

17.5.4 Multiple Frequency Selection for Dynamic Voltage–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

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