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E-raamat: Evolutionary Based Solutions for Green Computing

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Todays highly parameterized large-scale distributed computing systems may be composed  of a large number of various components (computers, databases, etc) and must provide a wide range of services. The users of such systems, located at different (geographical or managerial) network cluster may have a limited access to the systems services and resources, and different, often conflicting, expectations and requirements. Moreover, the information and data processed in such dynamic environments may be incomplete, imprecise, fragmentary, and overloading. All of the above mentioned issues require some intelligent scalable methodologies for the management of the whole complex structure, which unfortunately may increase the energy consumption of such systems. An optimal energy utilization has reached to a point that many information technology (IT) managers and corporate executives are all up in arms to identify scalable solution that can reduce electricity consumption (so that the total cost of operation is minimized) of their respective large-scale computing systems and simultaneously improve upon or maintain the current throughput of the system. 

This book in its eight chapters, addresses the fundamental issues related to the energy usage and the optimal low-cost system design in high performance ``green computing systems. The recent evolutionary and general metaheuristic-based solutions for energy optimization in data processing, scheduling, resource allocation, and communication in modern computational grids, could and network computing are presented along with several important conventional technologies to cover the hot topics from the fundamental theory of the green computing concept and to describe the basic architectures of systems. This book points out the potential application areas and provides detailed examples of application case studies in low-energy computational systems. The development trends and open research issuesare also outlined.  All of those technologies have formed the foundation for the green computing that we know of today.

Arvustused

From the reviews:

The editors and authors of this collection of papers intend to show the relevance of evolutionary computation in a set of established energy-saving applications . As a result, the relevance of the volume is to researchers in the same field, and to other researchers in green computing who are looking for approaches to solve optimization problems. The volume comprises eight chapters devoted to selected high-performance computing and network subsystem energy-saving efforts. (L.-F. Pau, ACM Computing Reviews, January, 2013)

1 Evolutionary Green Computing Solutions for Distributed Cyber Physical Systems
1(28)
Zahra Abbasi
Michael Jonas
Ayan Banerjee
Sandeep Gupta
Georgios Varsamopoulos
1.1 Introduction
2(1)
1.2 Green Computing in DCPS Domains
3(5)
1.2.1 Data Centers
4(1)
1.2.2 WSNs
5(1)
1.2.3 BSNs
5(1)
1.2.4 Problems Statements for Green Computing in DCPS
6(2)
1.3 Overview on EA
8(2)
1.4 EA Applications for Green Computing in DCPS
10(13)
1.4.1 Survey on Evolutionary-Based Solutions for Energy Aware Workload Scheduling in High Performance Computing (HPC) Data Centers
10(3)
1.4.2 Survey on Energy Efficient Routing Problem for WSNs
13(3)
1.4.3 Survey on Applications of EA for Thermal Aware Job Scheduling in HPC Data Centers
16(5)
1.4.4 Survey on Thermal Aware Communication Scheduling in Implanted Biosensor Networks
21(1)
1.4.5 Survey on Energy Harvesting and Cost Management in Data Centers
22(1)
1.5 Conclusion
23(6)
References
25(4)
2 Energy-Aware Provisioning of HPC Services through Virtualised Web Services
29(26)
Alexander Kipp
Tao Jiang
Jia Liu
Mariagrazia Fugini
Ionut Anghel
Tudor Cioara
Daniel Moldovan
Ioan Salomie
2.1 Introduction
30(2)
2.2 Scientific Workflows with Common Workflow Description Languages
32(4)
2.2.1 Requirements for Scientific Workflow Environments
32(1)
2.2.2 Applying Common Workflow Description Languages to the Scientific Computing Domain
33(3)
2.3 Virtualisation Infrastructure
36(6)
2.3.1 General Architecture
36(4)
2.3.2 Applying the Gateway Infrastructure to Different Domains
40(2)
2.4 Energy-Aware Job Scheduling and Deployment
42(5)
2.5 An Example: HPC Workflow
47(2)
2.6 Evaluation
49(1)
2.7 Concluding Remarks
50(5)
References
51(4)
3 Macro Level Models of Power Consumption for Servers in Distributed Systems
55(40)
Tomoya Enokido
Takuro Inoue
Alisher Aikebaire
Makoto Takizawa
3.1 Introduction
56(2)
3.2 Related Studies
58(1)
3.3 System Model
59(3)
3.3.1 Servers and Clients
59(1)
3.3.2 Processes in Servers
60(2)
3.4 Experimentations
62(7)
3.4.1 CP Applications
62(2)
3.4.2 CM Applications
64(2)
3.4.3 ST Applications
66(3)
3.5 Power Consumption Models
69(14)
3.5.1 CP Applications
69(7)
3.5.2 CM Applications
76(2)
3.5.3 ST Applications
78(5)
3.6 Server Selection Algorithms
83(5)
3.6.1 CP Applications
83(1)
3.6.2 CM Applications
84(2)
3.6.3 ST Applications
86(2)
3.7 Evaluation
88(3)
3.7.1 CP Applications
88(2)
3.7.2 CM Applications
90(1)
3.8 Conclusion
91(4)
References
92(3)
4 Energy and Security Awareness in Evolutionary-Driven Grid Scheduling
95(44)
Joanna Kolodziej
Samee U. Khan
Lizhe Wang
Dan Chen
Albert Y. Zomaya
4.1 Introduction
96(1)
4.2 Generic Model of Secure Grid Cluster
97(2)
4.3 Scheduling Problems in Computational Grids
99(4)
4.3.1 Problems Notation and Classification
101(2)
4.4 Independent Batch Scheduling Problem, Scheduling Scenarios and Objective Functions
103(9)
4.4.1 Expected Time to Compute (ETC) Matrix Model Adapted to Energy and Security Aware Scheduling in Grids
104(2)
4.4.2 Security Conditions
106(3)
4.4.3 Energy Model
109(3)
4.5 Security-Aware Genetic-Based Batch Schedulers
112(3)
4.6 Empirical Evaluation of Genetic Grid Schedulers
115(13)
4.6.1 Results
119(9)
4.7 Multi-population Genetic Grid Schedulers
128(4)
4.7.1 Empirical Analysis
130(1)
4.7.2 Results
130(2)
4.8 Related Work
132(3)
4.9 Conclusions
135(4)
References
136(3)
5 Power Consumption Constrained Task Scheduling Using Enhanced Genetic Algorithms
139(22)
Gang Shen
Yanqing Zhang
5.1 Introduction
139(2)
5.2 Power Consumption Constrained Task Scheduling Problem
141(3)
5.3 Enhanced Genetic Algorithm for the Green Task Scheduling Problem
144(7)
5.3.1 Genetic Algorithm
144(1)
5.3.2 Shadow Price Enhanced Genetic Algorithm
145(2)
5.3.3 Green Task Scheduling Using S PGA
147(4)
5.4 Performance Analysis
151(5)
5.5 Conclusions
156(5)
References
157(4)
6 Thermal Management in Many Core Systems
161(26)
Dhireesha Kudithipudi
Qinru Qu
Ayse K. Coskun
6.1 Introduction
161(1)
6.2 Thermal Monitoring
162(8)
6.2.1 Uniform Sensor Placement
163(1)
6.2.2 Non-uniform Sensor Placement
163(1)
6.2.3 Quality-Threshold Clustering
164(1)
6.2.4 K-Means Clustering
165(1)
6.2.5 Determining Thermal Hot Spots to Aid Sensor Allocation
166(2)
6.2.6 Non-uniform Subsampling of Thermal Maps
168(2)
6.3 Temperature Modeling and Prediction Techniques
170(7)
6.3.1 Thermal Modeling
171(2)
6.3.2 Temperature Prediction
173(4)
6.4 Runtime Thermal Management
177(5)
6.4.1 Model-Based Adaptive Thermal Management
177(5)
6.5 Conclusions
182(5)
References
183(4)
7 Sustainable and Reliable On-Chip Wireless Communication Infrastructure for Massive Multi-core Systems
187(40)
Amlan Ganguly
Partha Pande
Benjamin Belzer
Alireza Nojeh
7.1 Introduction
188(1)
7.2 Related Work
188(2)
7.3 Wireless NoC Architecture
190(9)
7.3.1 Topology
191(1)
7.3.2 Wireless Link Insertion and Optimization
192(3)
7.3.3 On-Chip Antennas
195(1)
7.3.4 Routing and Communication Protocols
196(3)
7.4 Performance Evaluations
199(12)
7.4.1 Establishment of Wireless Links
200(2)
7.4.2 Performance Metrics
202(1)
7.4.3 Performance Evaluation
203(8)
7.5 Reliability in WiNoCs
211(9)
7.5.1 Wireless Channel Model
212(3)
7.5.2 Proposed Product Code for the Wireless Links
215(1)
7.5.3 Residual BER of the Wireless Channel with H-PC
216(1)
7.5.4 Error Control Coding for the Wireline Links
217(3)
7.6 Experimental Results
220(3)
7.7 Conclusion
223(4)
References
223(4)
8 Exploiting Multi-Objective Evolutionary Algorithms for Designing Energy-Efficient Solutions to Data Compression and Node Localization in Wireless Sensor Networks
227
Francesco Marcelloni
Massimo Vecchio
8.1 Introduction
228(3)
8.2 Related Works
231(2)
8.2.1 Data Compression in WSN
231(1)
8.2.2 Node Localization in WSN
232(1)
8.3 Data Compression in WSN: An MOEA-Based Solution
233(3)
8.3.1 Problem Statement
233(1)
8.3.2 Overview of Our Approach
234(1)
8.3.3 Chromosome Coding and Mating Operators
235(1)
8.4 Node Localization in WSN: An MOEA-Based Solution
236(2)
8.4.1 Problem Statement
236(1)
8.4.2 Overview of Our Approach
237(1)
8.4.3 Chromosome Coding and Mating Operators
238(1)
8.5 Multi-Objective Evolutionary Algorithms
238(2)
8.5.1 NSGA-II
239(1)
8.5.2 PAES
239(1)
8.6 Experimental Results for the Data Compression Approach
240(7)
8.6.1 Experimental Setup
240(1)
8.6.2 Selecting an MOEA for the Specific Problem
241(1)
8.6.3 Experimental Results
242(2)
8.6.4 Comparison with LTC
244(3)
8.7 Experimental Results for the Node Localization Approach
247(5)
8.7.1 Experimental Setup
247(2)
8.7.2 Experimental Results and Comparisons
249(3)
8.8 Conclusions
252
References
253
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