Optimized cloud resource management and scheduling : theory and practice

✍ Scribed by Wenhong Tian

• Publisher: Morgan Kaufmann, , Elsevier Inc
• Year: 2015
• Tongue: English
• Leaves: 269
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Content:
Front-matter, Pages i,iii
Copyright, Page iv
Foreword, Page ix
Preface, Pages xi-xiii
About the Authors, Page xv
Acknowledgments, Page xvii
1 - An Introduction to Cloud Computing, Pages 1-15
2 - Big Data Technologies and Cloud Computing, Pages 17-49
3 - Resource Modeling and Definitions for Cloud Data Centers, Pages 51-77
4 - Cloud Resource Scheduling Strategies, Pages 79-93
5 - Load Balance Scheduling for Cloud Data Centers, Pages 95-114
6 - Energy-efficient Allocation of Real-time Virtual Machines in Cloud Data Centers Using Interval-packing Techniques, Pages 115-134
7 - Energy Efficiency by Minimizing Total Busy Time of Offline Parallel Scheduling in Cloud Computing, Pages 135-157
8 - Comparative Study of Energy-efficient Scheduling in Cloud Data Centers, Pages 159-178
9 - Energy Efficiency Scheduling in Hadoop, Pages 179-204
10 - Maximizing Total Weights in Virtual Machines Allocation, Pages 205-216
11 - A Toolkit for Modeling and Simulation of Real-time Virtual Machine Allocation in a Cloud Data Center, Pages 217-243
12 - Toward Running Scientific Workflows in the Cloud, Pages 245-266