Optimized Cloud Resource Management and Scheduling
Theories and Practices
- 1st Edition - October 16, 2014
- Latest edition
- Authors: Wenhong Dr. Tian, Yong Dr. Zhao
- Language: English
Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources… Read more
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
"This book offers an excellent overview of the state-of-the-art in resource scheduling and management in cloud computing. I strongly recommend the book as a reference for system architects, practitioners, developers, researchers and graduate level students."—Rajkumar Buyya, Editor in Chief, IEEE Transactions on Cloud Computing
- Edition: 1
- Latest edition
- Published: October 16, 2014
- Language: English
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 ChinaYZ
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 ChinaRead Optimized Cloud Resource Management and Scheduling on ScienceDirect