Energy-efficient distributed computing systems:
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2012
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| Schriftenreihe: | Wiley series on parallel and distributed computing
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XXXVI, 813 S. Ill., graph. Darst. 23 cm |
| ISBN: | 9780470908754 0470908750 |
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| 020 | |a 9780470908754 |c hbk £80.50 |9 978-0-470-90875-4 | ||
| 020 | |a 0470908750 |c hbk £80.50 |9 0-470-90875-0 | ||
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| 245 | 1 | 0 | |a Energy-efficient distributed computing systems |c ed. by Albert Y. Zomaya ; Young-Choon Lee |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2012 | |
| 300 | |a XXXVI, 813 S. |b Ill., graph. Darst. |c 23 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series on parallel and distributed computing | |
| 500 | |a Literaturangaben | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 0 | 7 | |a Rechnernetz |0 (DE-588)4070085-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Energieeffizienz |0 (DE-588)7660153-5 |2 gnd |9 rswk-swf |
| 653 | |a Computer networks / Energy conservation | ||
| 653 | |a Electronic data processing / Distributed processing / Energy conservation | ||
| 653 | |a Green technology | ||
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
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| 689 | 0 | 1 | |a Energieeffizienz |0 (DE-588)7660153-5 |D s |
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| 700 | 1 | |a Zomaya, Albert Y. |d 1964- |0 (DE-588)135767342 |4 edt | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297295&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804149509935071232 |
|---|---|
| adam_text | CONTENTS
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
і
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
S
1.2.5
Scheduling Models
9
1.2.6
Problem Decomposition
9
vii
VU!
CONTENTS
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
CONTENTS
ІХ
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 T {w,
f
) 60
CONTENTS
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-ZOFF-chip workload with
DOP
=
і
61
2.5.1.4
Power-aware speedup for
DOP
and
ON-ZOFF-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
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
CONTENTS
XI
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
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
ENERGY-EFFICIENT RESERVATION INFRASTRUCTURE
FOR GRIDS, CLOUDS, AND NETWORKS
133
Anne-Cécile
Orgerie and Laurent
Lefèvre
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
Xii
CONTENTS
5.3.2
Management of the Resource Reservations
141
5.3.3
Resource Management ami ()a t)l! Algorithms
145
5.3.4
Energy-Consumption
Estímales
14<->
5.3.5
Prediction Algorithms
14ή
5.4
F.ARI: Enemy-Aware
Resenation
Infrastructure for Data
Centers and Grids
14 ■
5.4.1
EARI s Architecture ¡4
5.4.2
Validation of EAR! on
Experimenta!
Grid Traces
147
5.5
GOC: Green Open Cloud
144
5.5.1
GOC
s
Resource Manager Architecture i?<»
5.5.2
Validation of the GOC Eramew
ork
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
Discussisi
15
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 I63
6.
1.1 Context 163
6.
1.
2
Chapter Roadmap 1
64
6.2
Energy-Aware Infrastructures 1
64
6.2.
1 Buildings 1
65
6.2.2
Context-Aware Buildings 1
65
6.2.3
Cooling
і
66
6.3
Current Resource Management Practices 1
67
6.3.
1 Widely Used Resource Management Systems 167
6.3.2
Job Requirement Description 1
69
6.4
Scientific and Technical Challenges 1
70
6.4.
1 Theoretical Difficulties 1
70
6.4.2
Technical Difficulties 1
70
6.4.3
Controlling and Tuning Jobs
17
і
6.5
Energy-Aware Job Placement Algorithms
172
CONTENTS
XIII
6.5.1
State of the Art
172
ň.
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
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
P
Es
191
7.2.1.2
DVS
191
7.2.1.3
Tusks
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-ma.x
198
7.3.1.5
MaxMin
199
7.3.1.6
ObFun
199
7.3.1.7
MinMinStdDev
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
^rge-size problems
206
7.5
Related Works
211
XIV
CONTENTS
7.6
Conclusion
211
References
212
8
TOWARD ENERGY-AWARE SCHEDULING USING
MACHINE LEARNING
215
Josep LL.
Berrai,
Iñigo
Goiri, Ramon
Nou,
Ferran Julia, Josep O.
Fitó,
Jordi Guitart,
Ricard
Gavaldá,
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
Autonomie
Computing Techniques
217
8.1.4
Power-Aware
Autonomie
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
CONTENTS
XV
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
XVI
CONTENTS
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
AUTONOMIO
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
Autonomie
Computing
282
10.3
Autonomie
Green Computing: A Case Study
283
10.3.1
Autonomie
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
CONTENTS
XVii
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
Autonomie
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
XVÜi CONTENTS
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
Perfonnance 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
CONTENTS
ХІХ
14
AUTONOMIO
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 Autonomie
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
XX
CONTENTS
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
^-Adaptation: A New DVFS Algorithm
423
16.3.1
The Compute-Boundedness Metric,
β
423
16.3.2
The Frequency Calculating Formula,
ƒ* 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
Babau
Rizvandi, Albert Y. Zomaya, Young Choon
Lee, Ali
Javadzadeh Boloori, and Javid Taheri
17.1
Introduction
17.2
Energy Efficiency in HPC Systems
17.3
Exploitation of Dynamic Voltage-Frequency Scaling
17.3.1
Independent Slack Reclamation
CONTENTS
ХХІ
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
Winte! versus ARM
508
18.5.4
Reconfigurable
Computing is the Silver Bullet
511
XXii
CONTENTS
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
18.5.7
How to Reinvent Computing
18.6
Conclusions
References
19
WORKLOAD CLUSTERING FOR INCREASING ENERGY
SAVINGS ON EMBEDDED MPSOCS
Ozcan Ozturk, Mahmut Kandemir, and Sri Hari Krishna
Narayanan
19.1
Introduction
19.2
Embedded MPSoC Architecture, Execution Model, and
Related Work
19.3
Our Approach
19.3.1
Overview
19.3.2
Technical Details and Problem Formulation
19.3.2.1
System and job model
19.3.2.2
Mathematical programing model
19.3.2.3
Example
19.4
Experimental Evaluation
19.5
Conclusions
References
20
ENERGY-EFFICIENT INTERNET INFRASTRUCTURE
Weirong Jiang and Viktor K. Prasanna
20.1
Introduction
20.1.1
Performance Challenges
20.1.2
Existing Packet Forwarding Approaches
20.1.2.1
Software approaches
20.1.2.2
Hardware approaches
20.2
SRAM-Based Pipelined IP Lookup Architectures:
Alternative to TCAMs
20.3
Data Structure Optimization for Power Efficiency
20.3.1
Problem Formulation
CONTENTS Xxiii
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
XXIV
CONTENTS
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
22.23.1
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
МЕ
-MC
heuristic
622
22.2.3.5
ME
-МЕ
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
CONTENTS XXV
22.3.3.7 Minimum
completion time
(МСТ)
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
Flávia
С.
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
XXVI
CONTENTS
24.3.2
Intemode
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
Flávia
С.
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
CONTENTS
XXVII
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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| indexdate | 2024-07-10T00:24:10Z |
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| language | English |
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| physical | XXXVI, 813 S. Ill., graph. Darst. 23 cm |
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| series2 | Wiley series on parallel and distributed computing |
| spelling | Energy-efficient distributed computing systems ed. by Albert Y. Zomaya ; Young-Choon Lee Hoboken, NJ Wiley 2012 XXXVI, 813 S. Ill., graph. Darst. 23 cm txt rdacontent n rdamedia nc rdacarrier Wiley series on parallel and distributed computing Literaturangaben Datenverarbeitung Rechnernetz (DE-588)4070085-9 gnd rswk-swf Energieeffizienz (DE-588)7660153-5 gnd rswk-swf Computer networks / Energy conservation Electronic data processing / Distributed processing / Energy conservation Green technology (DE-588)4143413-4 Aufsatzsammlung gnd-content Rechnernetz (DE-588)4070085-9 s Energieeffizienz (DE-588)7660153-5 s DE-604 Zomaya, Albert Y. 1964- (DE-588)135767342 edt Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297295&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Energy-efficient distributed computing systems Datenverarbeitung Rechnernetz (DE-588)4070085-9 gnd Energieeffizienz (DE-588)7660153-5 gnd |
| subject_GND | (DE-588)4070085-9 (DE-588)7660153-5 (DE-588)4143413-4 |
| title | Energy-efficient distributed computing systems |
| title_auth | Energy-efficient distributed computing systems |
| title_exact_search | Energy-efficient distributed computing systems |
| title_full | Energy-efficient distributed computing systems ed. by Albert Y. Zomaya ; Young-Choon Lee |
| title_fullStr | Energy-efficient distributed computing systems ed. by Albert Y. Zomaya ; Young-Choon Lee |
| title_full_unstemmed | Energy-efficient distributed computing systems ed. by Albert Y. Zomaya ; Young-Choon Lee |
| title_short | Energy-efficient distributed computing systems |
| title_sort | energy efficient distributed computing systems |
| topic | Datenverarbeitung Rechnernetz (DE-588)4070085-9 gnd Energieeffizienz (DE-588)7660153-5 gnd |
| topic_facet | Datenverarbeitung Rechnernetz Energieeffizienz Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297295&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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