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Energy efficient distributed computing systems / edited by Albert Y. Zomaya, Young Choon Lee

Published:
Hoboken, N.J. : Wiley, [2012]
Copyright Date:
©2012
Physical Description:
1 online resource (813 pages) : illustrations
Additional Creators:
Zomaya, Albert Y. and Lee, Young-Choon, 1973-
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Language Note:
English.
Contents:
Chapter 1. Power Allocation and Task Scheduling on Multiprocessor Computers with Energy and Time Constraints -- Chapter 2. Power-Aware High Performance Computing -- Chapter 3. Energy Efficiency in HPC Systems -- Chapter 4. A Stochastic Framework for Hierarchical System-Level Power Management -- Chapter 5. Energy-Efficient Reservation Infrastructure for Grids, Clouds, and Networks -- Chapter 6. Energy-Efficient Job Placement on Clusters, Grids, and Clouds -- Chapter 7. Comparison and Analysis of Greedy Energy-Efficient Scheduling Algorithms for Computational Grids -- Chapter 8. Toward Energy-Aware Scheduling Using Machine Learning -- Chapter 9. Energy Efficiency Metrics for DATA Centers -- Chapter 10. Autonomic Green Computing in Large-Scale Data Centers -- Chapter 11. Energy and Thermal Aware Scheduling in Data Centers -- Chapter 12. QOS-Aware Power Management in Data Centers -- Chapter 13. Energy-Efficient Storage Systems for Data Centers -- Chapter 14. Autonomic Energy/Performance Optimizations for Memory in Servers -- Chapter 15. ROD: A Practical Approach to Improving Reliability of Enegery-Efficient Parallel Disk Systems -- Chapter 16. Embracing the Memory and I/O Walls for Energy-Efficient Scientific Computing -- Chapter 17. Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption -- Chapter 18. The Paramountcy of Reconfigurable Computing -- Chapter 19. Workload Clustering for Increasing Energy Savings on Embedded MPSOCS -- Chapter 20. Energy-Efficient Internet Infrastructure -- Chapter 21. Demand Response in the Smart Grid: A Distributed Computing Perspective -- Chapter 22. Resource Management for Distributed Mobile Computing -- Chapter 23. An Energy-Aware Framework for Mobile Data Mining -- Chapter 24. Energy Awareness and Efficiency in Wireless Sensor Networks: From Physical Devices to the Communication Link -- Chapter 25. Network-Wide Strategies for Enrgy Efficiency in Wireless Sensor Networks -- Chapter 26. Energy Management in Heterogeneous Wireless Health Care Networks. and Contents note continued: 9.1.3.1. Electric power -- 9.1.3.2. Heat removal -- 9.1.4. Energy Efficiency -- 9.2. Fundamentals of Metrics -- 9.2.1. Demand and Constraints on Data Center Operators -- 9.2.2. Metrics -- 9.2.2.1. Criteria for good metrics -- 9.2.2.2. Methodology -- 9.2.2.3. Stability of metrics -- 9.3. Data Center Energy Efficiency -- 9.3.1. Holistic IT Efficiency Metrics -- 9.3.1.1. Fixed versus proportional overheads -- 9.3.1.2. Power versus energy -- 9.3.1.3. Performance versus productivity -- 9.3.2. Code of Conduct -- 9.3.2.1. Environmental statement -- 9.3.2.2. Problem statement -- 9.3.2.3. Scope of the CoC -- 9.3.2.4. Aims and objectives of CoC -- 9.3.3. Power Use in Data Centers -- 9.3.3.1. Data center IT power to utility power relationship -- 9.3.3.2. Chiller efficiency and external temperature -- 9.4. Available Metrics -- 9.4.1. Green Grid -- 9.4.1.1. Power usage effectiveness (PUE) -- 9.4.1.2. Data center efficiency (DCE) -- 9.4.1.3. Data center infrastructure efficiency (DCiE) -- 9.4.1.4. Data center productivity (DCP) -- 9.4.2. McKinsey -- 9.4.3. Uptime Institute -- 9.4.3.1. Site infrastructure power overhead multiplier (SI-POM) -- 9.4.3.2. IT hardware power overhead multiplier (H-POM) -- 9.4.3.3. DC hardware compute load per unit of computing work done -- 9.4.3.4. Deployed hardware utilization ratio (DH-UR) -- 9.4.3.5. Deployed hardware utilization efficiency (DH-UE) -- 9.5. Harmonizing Global Metrics for Data Center Energy Efficiency -- References -- 10. Autonomic Green Computing In Large-Scale Data Centers / Youssif Al-Nashif -- 10.1. Introduction -- 10.2. Related Technologies and Techniques -- 10.2.1. Power Optimization Techniques in Data Centers -- 10.2.2. Design Model -- 10.2.3. Networks -- 10.2.4. Data Center Power Distribution -- 10.2.5. Data Center Power-Efficient Metrics -- 10.2.6. Modeling Prototype and Testbed -- 10.2.7. Green Computing -- 10.2.8. Energy Proportional Computing -- 10.2.9. Hardware Virtualization Technology -- 10.2.10. Autonomic Computing -- 10.3. Autonomic Green Computing: A Case Study -- 10.3.1. Autonomic Management Platform -- 10.3.1.1. Platform architecture -- 10.3.1.2. DEVS-based modeling and simulation platform -- 10.3.1.3. Workload generator -- 10.3.2. Model Parameter Evaluation -- 10.3.2.1. State transitioning overhead -- 10.3.2.2. VM template evaluation -- 10.3.2.3. Scalability analysis -- 10.3.3. Autonomic Power Efficiency Management Algorithm (Performance Per Watt) -- 10.3.4. Simulation Results and Evaluation -- 10.3.4.1. Analysis of energy and performance trade-offs -- 10.4. Conclusion and Future Directions -- References -- 11. Energy And Thermal Aware Scheduling In Data Centers / Tajana S. Rosing -- 11.1. Introduction -- 11.2. Related Work -- 11.3. Intermachine Scheduling -- 11.3.1. Performance and Power Profile of VMs -- 11.3.2. Architecture -- 11.3.2.1. vgnode -- 11.3.2.2. vgxen -- 11.3.2.3. vgdom -- 11.3.2.4. vgserv -- 11.4. Intramachine Scheduling -- 11.4.1. Air-Forced Thermal Modeling and Cost -- 11.4.2. Cooling Aware Dynamic Workload Scheduling -- 11.4.3. Scheduling Mechanism -- 11.4.4. Cooling Costs Predictor -- 11.5. Evaluation -- 11.5.1. Intermachine Scheduler (vGreen) -- 11.5.2. Heterogeneous Workloads -- 11.5.2.1. Comparison with DVFS policies -- 11.5.2.2. Homogeneous workloads -- 11.5.3. Intramachine Scheduler (Cool and Save) -- 11.5.3.1. Results -- 11.5.3.2. Overhead of CAS -- 11.6. Conclusion -- References -- 12. QOS-Aware Power Management In Data Centers / Cheng-Zhong Xu -- 12.1. Introduction -- 12.2. Problem Classification -- 12.2.1. Objective and Constraint -- 12.2.2. Scope and Time Granularities -- 12.2.3. Methodology -- 12.2.4. Power Management Mechanism -- 12.3. Energy Efficiency -- 12.3.1. Energy-Efficiency Metrics -- 12.3.2. Improving Energy Efficiency -- 12.3.2.1. Energy minimization with performance guarantee -- 12.3.2.2. Performance maximization under power budget -- 12.3.2.3. Trade-off between power and performance -- 12.3.3. Energy-Proportional Computing -- 12.4. Power Capping -- 12.5. Conclusion -- References -- 13. Energy-Efficient Storage Systems For Data Centers / Anand Sivasubramaniam -- 13.1. Introduction -- 13.2. Disk Drive Operation and Disk Power -- 13.2.1. Overview of Disk Drives -- 13.2.2. Sources of Disk Power Consumption -- 13.2.3. Disk Activity and Power Consumption -- 13.3. Disk and Storage Power Reduction Techniques -- 13.3.1. Exploiting the STANDBY State -- 13.3.2. Reducing Seek Activity -- 13.3.3. Achieving Energy Proportionality -- 13.3.3.1. Hardware approaches -- 13.3.3.2. Software approaches -- 13.4. Using Nonvolatile Memory and Solid-State Disks -- 13.5. Conclusions -- References -- 14. Autonomic Energy/Performance Optimizations For Memory In Servers / Mazin Yousif -- 14.1. Introduction -- 14.2. Classifications of Dynamic Power Management Techniques -- 14.2.1. Heuristic and Predictive Techniques -- 14.2.2. QoS and Energy Trade-Offs -- 14.3. Applications of Dynamic Power Management (DPM) -- 14.3.1. Power Management of System Components in Isolation -- 14.3.2. Joint Power Management of System Components -- 14.3.3. Holistic System-Level Power Management -- 14.4. Autonomic Power and Performance Optimization of Memory Subsystems in Server Platforms -- 14.4.1. Adaptive Memory Interleaving Technique for Power and Performance Management -- 14.4.1.1. Formulating the optimization problem -- 14.4.1.2. Memory appflow -- 14.4.2. Industry Techniques -- 14.4.2.1. Enhancements in memory hardware design -- 14.4.2.2. Adding more operating states -- 14.4.2.3. Faster transition to and from low power states -- 14.4.2.4. Memory consolidation -- 14.5. Conclusion -- References -- 15. ROD: A Practical Approach To Improving Reliability Of Energy-Efficient Parallel Disk Systems / Xiao Qin -- 15.1. Introduction -- 15.2. Modeling Reliability of Energy-Efficient Parallel Disks -- 15.2.1. MINT Model -- 15.2.1.1. Disk utilization -- 15.2.1.2. Temperature -- 15.2.1.3. Power-state transition frequency -- 15.2.1.4. Single disk reliability model -- 15.2.2. MAID, Massive Arrays of Idle Disks -- 15.3. Improving Reliability of MAID via Disk Swapping -- 15.3.1. Improving Reliability of Cache Disks in MAID -- 15.3.2. Swapping Disks Multiple Times -- 15.4. Experimental Results and Evaluation -- 15.4.1. Experimental Setup -- 15.4.2. Disk Utilization -- 15.4.3. Single Disk Swapping Strategy -- 15.4.4. Multiple Disk Swapping Strategy -- 15.5. Related Work -- 15.6. Conclusions -- References -- 16. Embracing The Memory And I/O Walls For Energy-Efficient Scientific Computing / Wu-Chun Feng -- 16.1. Introduction -- 16.2. Background and Related Work -- 16.2.1. DVFS-Enabled Processors -- 16.2.2. DVFS Scheduling Algorithms -- 16.2.3. Memory-Aware, Interval-Based Algorithms -- 16.3. β-Adaptation: A New DVFS Algorithm -- 16.3.1. Compute-Boundedness Metric, β -- 16.3.2. Frequency Calculating Formula, f* -- 16.3.3. Online β Estimation -- 16.3.4. Putting It All Together -- 16.4. Algorithm Effectiveness -- 16.4.1. Comparison to Other DVFS Algorithms -- 16.4.2. Frequency Emulation -- 16.4.3. Minimum Dependence to the PMU -- 16.5. Conclusions and Future Work -- References -- 17. Multiple Frequency Selection In Dvfs-Enabled Processors To Minimize Energy Consumption / 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 -- 17.3.2. Integrated Schedule Generation -- 17.4. Preliminaries -- 17.4.1. System and Application Models -- 17.4.2. Energy Model -- 17.5. Energy-Aware Scheduling via DVFS -- 17.5.1. Optimum Continuous Frequency -- 17.5.2. Reference Dynamic Voltage-Frequency Scaling (RDVFS) -- 17.5.3. Maximum-Minimum-Frequency for Dynamic Voltage-Frequency Scaling (MMF-DVFS) -- 17.5.4. Multiple Frequency Selection for Dynamic Voltage-Frequency Scaling (MFS-DVFS) -- 17.5.4.1. Task eligibility -- 17.6. Experimental Results -- 17.6.1. Simulation Settings -- 17.6.2. Results -- 17.7. Conclusion -- References -- 18. Paramountcy Of Reconfigurable Computing
Summary:
"The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers that investigate the different facets of green and energy efficient distributed computing. Key features: One of the first books of its kind Features latest research findings on emerging topics by well-known scientists Valuable research for grad students, postdocs, and researchers Research will greatly feed into other technologies and application domains"--
Subject(s):
Genre(s):
ISBN:
9781118342015 (electronic bk.)
1118342011 (electronic bk.)
9781118341988 (electronic bk.)
1118341988 (electronic bk.)
1283546019
9781283546010
9780470908754 (hardback)
0470908750 (hardback)
9786613858467
6613858463
1118342003
9781118342008
Digital File Characteristics:
data file
Bibliography Note:
Includes bibliographical references and index.
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