Optimized Cloud Resource Management and Scheduling: Theories and Practices

Overview

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 ...
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Optimized Cloud Resource Management and Scheduling: Theories and Practices

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Overview

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
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Editorial Reviews

From the Publisher
“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
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Product Details

  • ISBN-13: 9780128014769
  • Publisher: Elsevier Science
  • Publication date: 10/30/2014
  • Pages: 284
  • Product dimensions: 6.00 (w) x 8.90 (h) x 0.70 (d)

Meet the Author

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.

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.

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Table of Contents

1 Cloud computing overview /1
2 Overview of Solutions to Cloud Data Centers /1
3 Resources Modeling and Definitions in Cloud data centers
5 Cloud Resource Scheduling Strategies /1
6 Load Balance Scheduling Algorithms For CDC /1
7 Energy-efficient Scheduling for Cloud Data Centers
8 Maximizing Profits of Computing Resources in CDC
9 Energy-Efficiency Scheduling in Hadoop
10 Cloud Workflow Management and Applications
11 The Design and Application of Cloud Simulators
12 Summary and Outlook

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