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Optimized Cloud Resource Management and Scheduling
Theories and Practices
1st Edition - October 15, 2014
Authors: Wenhong Dr. Tian, Yong Dr. Zhao
Language: English
eBook ISBN:9780128016459
9 7 8 - 0 - 1 2 - 8 0 1 6 4 5 - 9
Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources…Read more
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Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources in cloud data centers supporting scientific, industrial, business, and consumer applications. It serves as a valuable reference for systems architects, practitioners, developers, researchers and graduate level students.
Explains how to optimally model and schedule computing resources in cloud computing
Provides in depth quality analysis of different load-balance and energy-efficient scheduling algorithms for cloud data centers and Hadoop clusters
Introduces real-world applications, including business, scientific and related case studies
Discusses different cloud platforms with real test-bed and simulation tools
academic/research, graduate students, professionals, professional computer science developers and graduate students especially at Masters level.
Foreword
Preface
About the Authors
Acknowledgments
1. An Introduction to Cloud Computing
Main Contents of this Chapter
1.1 The background of Cloud computing
1.2 Cloud computing is an integration of other advanced technologies
1.3 The driving forces of Cloud computing
1.4 The development status and trends of Cloud computing
1.5 The classification of Cloud computing applications
1.6 The different roles in the Cloud computing industry chain
1.7 The main features and technical challenges of Cloud computing
Summary
References
2. Big Data Technologies and Cloud Computing
Main Contents of this Chapter
2.1 The background and definition of big data
2.2 Big data problems
2.3 The dialectical relationship between Cloud computing and big data
2.4 Big data technologies
Summary
Acknowledgments
References
3. Resource Modeling and Definitions for Cloud Data Centers
Main Contents of this Chapter
3.1 Resource models in Cloud data centers
3.2 Data center resources
3.3 Categories of Cloud data center resources
3.4 Constraints and dependencies among resources
3.5 Data modeling of resources in a Cloud data center
3.6 Conclusion
Appendix 1: The UML Relationship of Resources
References
4. Cloud Resource Scheduling Strategies
Main Contents of this Chapter
4.1 Key technologies of resource scheduling
4.2 Comparative analysis of scheduling strategies
4.3 Classification of main scheduling strategies
4.4 Some constraints of scheduling strategies
4.5 Scheduling task execution time and trigger conditions
Summary
Appendix: Some elementary terms
References
5. Load Balance Scheduling for Cloud Data Centers
Main Contents of this Chapter
5.1 Introduction
5.2 Related work
5.3 Problem formulation and description
5.4 OLRSA algorithm
5.5 LIF algorithm
5.6 Discussion and conclusion
References
6. Energy-efficient Allocation of Real-time Virtual Machines in Cloud Data Centers Using Interval-packing Techniques
Main Contents of this Chapter
6.1 Introduction
6.2 GreenCloud architecture
6.3 Energy-efficient real-time scheduling
6.4 Performance evaluation
6.5 Related work
6.6 Conclusions
References
7. Energy Efficiency by Minimizing Total Busy Time of Offline Parallel Scheduling in Cloud Computing
Main Contents of this Chapter:
7.1 Introduction
7.2 Approximation algorithm and its approximation ratio bound
7.3 Application to energy efficiency in Cloud computing
7.4 Performance evaluation
7.5 Conclusions
References
8. Comparative Study of Energy-efficient Scheduling in Cloud Data Centers
Main Contents of this Chapter:
8.1 Introduction
8.2 Related research
8.3 Comparative study of offline scheduling algorithms
8.4 Online algorithms
8.5 Summary
References
9. Energy Efficiency Scheduling in Hadoop
Main Contents of this Chapter:
9.1 Overview
9.2 Scheduling algorithms
9.3 Energy control
9.4 Energy-efficient scheduling for multiple users
9.5 Performance evaluation
9.6 Summary
Questions
References
10. Maximizing Total Weights in Virtual Machines Allocation
Main Contents of this Chapter
10.1 Introduction
10.2 Problem formulation: WISWCS
10.3 WISWCS
10.4 An exact SAWISWCS
10.5 Applications of WISWCS
10.6 Related work
10.7 Conclusions
References
11. A Toolkit for Modeling and Simulation of Real-time Virtual Machine Allocation in a Cloud Data Center
Main Contents of this Chapter
11.1 Introduction of the cloud data center
11.2 The architecture and main features of CloudSched
11.3 Performance metrics for different scheduling algorithms
11.4 Design and implementation of CloudSched
11.5 Performance evaluation
11.6 Conclusions
References
12. Toward Running Scientific Workflows in the Cloud
Main Contents of this Chapter
12.1 Introduction
12.2 Related work
12.3 Integration
12.4 Experiment
12.5 Experiment on Amazon EC2
12.6 Conclusions
References
No. of pages: 284
Language: English
Edition: 1
Published: October 15, 2014
Imprint: Morgan Kaufmann
eBook ISBN: 9780128016459
WT
Wenhong Dr. Tian
Dr. Wenhong Tian has a PhD from Computer Science Department of North Carolina State University(NCSU) and did post-doc with joint funding from Ork Ridge National Lab and NCSU. He is now an associate professor at University of Electronic Science and Technology of China. His research interests include modeling and performance analysis of communication networks, Cloud computing and bio-computing. He has published more than 40 journal /conference papers in related areas.
Affiliations and expertise
Associate Professor at University of Electronic Science and Technology of China
YZ
Yong Dr. Zhao
Prof. Yong Zhao has a PhD from Computer Science Department of Chicago University (under supervising of Prof. Ian Foster); his is now a professor at University of Electronic Science and Technology of China. His research interests include Grid computing, large-data process in Cloud computing etc. He published about 30 journal and conference papers in related areas.
Affiliations and expertise
Associate Professor at the University of Electronic Science and Technology of China
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