Optimized Cloud Resource Management and Scheduling
Theories and Practices

 

 by :

 Wenhong Tian, Yong Zhao

 Press :

 Morgan Kaufmann

 Year :

 2015

 Binding :

 Paperback

 ISBN (13) :

 9780128014769

 ISBN (10) :

 0128014768

 Pages :

 266

 Size :

 16 x 23

 Price :

 $ 79.95

 

 

 
Captures the state-of-the-art in resource management and scheduling in cloud data centers

Overview

 Key Features

  • 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

 

Description

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.

Readership

academic/research, graduate students, professionals, professional computer science developers and graduate students especially at Masters level.

Contents


PREFACE

ACKNOWLEDGMENTS  


Chapter 1 Cloud computing overview /1

1.1 Background of Cloud computing /2
1.2 Cloud computing and other advanced technologies /4
1.3 Why do we need Cloud computing? /8
1.4 Development status and trends of Cloud computing /9
1.5.1 Classified by service types /11
1.5.2 Classified by deployment methods /12
1.6 different roles in Cloud computing industry chain /13
1.7 The main features and challenges of Cloud computing /13
1.7.1 The main features of Cloud computing /13
1.7.2 Challenging issues /14
1.8 summary /16
 References 

 

Chapter  2  Overview of Solutions to  Cloud Data Centers /1

 

2.1 Overview of Cloud data centers /2
2.1.1 Introduction to Cloud data centers /2
2.1.2 Requirements and challenges of Cloud data centers /4
2.2 Requirement analysis of resource scheduling in Cloud data centers /4
2.2.1 Technical Requirements  /4
2.2.2 Technical Objectives /6
2.3 research development of resource scheduling in Cloud data centers /6
2.4 Analysis of solutions in Cloud data centers  /7
2.4.1 Google Solutions 7
2.4.2 Amazon solutions 8
2.4.3 IBM Solutions 10
2.4.5 HP Solutions 11
2.4.6 VMWare Solutions /12
2.4.7 Open Source Solutions  /13
2.5 Progress of Cloud computing resource scheduling standards /15
2.6 key technology and research focus of Cloud resource scheduling /16
2.7 Summary  /18
Quiz /18
References /19

 

Chap3   Resources Modeling and Definitions in Cloud data centers
 

3. 1 Overview 2
3.2 Categories of Cloud Data Center Resources 3
3.3 Properties and Operation of Resources 3
3.3.1 Physical servers (PMs) 3
3.3.1.1  The Main Properties of a Physical Server 3
3.4 Constraints and Dependencies among Resources 14
3.4.1  Software/Hardware Based Relations 15
3.4.2 Associated Hardware/Software Platforms and Networks 15
3.4.3 Reliability Constraints 15
3.4.4 Time Constraints 15
3.4.5 The relationship among performance , system capacity (storage) and bandwidth 15
3.5 Data Modeling of Resource in Cloud Datacenter 15
3.6 The Relationship of Resources 15
3.7 Data Management of Main Resource 15
3.7.1 Data Center 15
3.7.2 Schedule Domain 16
3.7.3 Query physical machine 16
3.7.4 Add Physical Machine 16
3.7.5 Delete Physical Machine 16
3.7.6 Update the information of physical machine 17
3.7.7 Query Virtual Machine 17
3.7.8 Add Virtual Machine 17
3.7.9 Delete Virtual Machine 18
3.7.10 Update Virtual Machine Information 18

References 18


Chapter 5 Cloud Resource Scheduling Strategies /1

5.1  Key Technologies of Resource Scheduling /3
5.2  Comparative Analysis of Scheduling Strategies  /4
5.2.1 Amazon  / 4
5.2.2 IBM  /5
5.2.2.1 Performance Related:  User Requirements Satified /5
5.2.3 HP  /7
5.2.3.1 Cost Based: Cost Model 7
5.2.3.2 Load-balance: automatically place virtual machine and dynamic migration /7
5.2.4 VMWare  /8
5.2.4.1 Improve Resource Utilization  /8
5.2.4.2 Improve Reliability /8
5.2.4.3 Load Balance: Distributed Resource Scheduling (DRS) /9
5.2.5 Other Solutions /9
5.2.5.1 Fair Scheduling /10
5.2.5.2 Load balance /10
5.2.5.3 Delay Scheduling for Locality /10
5.2.5.4 Improve Reliability /10
5.3   Classification of Main Scheduling Strategies /11
5.3.1 Performance Related /11
5.3.1.1 First Come First Service /11
5.3.1.2 Load-Balance /11
5.3.1.3 Improve Reliability /12
5.3.2 Cost Based /13
5.3.2.1 Improve Overall Utilization /13
5.3.2.2 Maximum Profit /13
5.3.2.1 Minimum Operation Costs /14
5.3.2.2 Combining Scheduling Strategies /16
5.4 Some Constraints of Scheduling Strategies /18
5.4.1 Space: association and anti-association  /18
5.4.2 Schedule Domain: scheduling locality /18
5.4.3 Time: limited available time  /18
5.4.4 Migration vs. Non-migratory /18
5.5 Schedule Task Execution Time and Trigger Condition /19
5.6 Summary  /19
Elementary Terms /19
Questions /21
References /21


Chapter 6               Load Balance Scheduling Algorithms For CDC /1

6.1 Overview   /3
6.2  Comparative Study of  Traditional Load Balance Scheduling Algorithms /4
6.2.1 Round-Robin Algorithm (RR) /4
6.2.2 Weighted Round-Robin Algorithm (wRR) /5
6.2.3 Destination Hashing Scheduling (DH) /6
6.2.4 Source Hashing Scheduling (SH) /7
6.2.5  Least Connected (LC) /7
6.2.6 Weighted Least Connected (WLC) /8
6.3 Dynamic integrated load balance scheduling algorithms /9
6.3.1 Integrated Utilization Product Method /9
6.3.2 Integrated Load Benchmarks Method /10
6.3.3 Dynamic Feedback  Algorithm /11
6.4 Comparison of Load Balance Algorithms 11
6.5 Detail Design of Balance with Dynamic Feedback Algorithm 16
6.5.1 Optimization Targets 16
6.5.1.1 Optimization Targets Introduction 16
6.5.1.2 Definition of Algorithms Parameters 16
6.5.2 Balance with Dynamic Feedback Algorithm 18
6.5.2.1 Details of Algorithm 18
6.5.2.2 Inputs of Algorithm 18
6.5.2.3 Outputs of Algorithm 18
6.5.2.4 Predefined Condition and Constraints of Algorithm 19
6.6   Online Scheduling Algorithms for CDC /21
6.7  Offline  Scheduling Algorithms for CDC /26
6.8 Summary   /31
Questions 21
Reference

 

Chapter 7 Energy-efficient Scheduling for Cloud Data Centers 


7.1 Introduction  /2
7.2 Research Objectives /3
7.3 Comparative Study of Offline Scheduling Algorithms  /7
7.3.1 Data Center Energy Models / 7
7.3.2  FFD Algorithm      /10
7.3.3 MFFDE  algorithm    /12
7.3.4  Other Offline Algorithms                                             /15
7.4  Online Algorithms  /18
7.4.1 GREEDYBUCKET / 20
7.4.2  GRID      /25
7.4.3 Dynamic Round Robin     /30
7.5 Summary /36
Questions
References


Chapter 8 Maximizing Profits of Computing Resource in CDC

8.1  Overview /3
8.1.1 Cloud Computing and Data Center /3
8.1.2 Computing Resources in Cloud Data Centers
 
8.2 Introduction to interval scheduling problem
8.2.1 Interval Scheduling   /5
8.3 Sharing Capacity Weighted Interval Scheduling
8.3.1 Traditional Weighted Interval Scheduling Problem (WIS) / 6
8.3.2 Mutually Compatible Intervals
8.4 Weighted Interval Scheduling with Capacity Sharing (WISWCS)
8.4.1 Mutually Sharing Compatible Intervals  9
8.4.3  Capacity Partition  9
8.4.4 WISWCS Algorithm with weights proportional to the capacity 9
8.4.6  Proof of Algorithm Correctness  11
8.4.7 Computation Complexity Analysis 12
8.4.8 Analysis of the required minimum number of  physical machines
8.5 Application Discussion 13
8.5.1 Virtual Machine Scheduling 13
8.5.2 Communication Networks 13
8.5.3  Others. 13
8.6 Related Work 13
8.7 Summary 14
Questions
References

 

Chapter 9  Energy-Efficiency Scheduling in Hadoop 


9.1 Overview /3
9.1.1 Hadoop Introduction 3
9.1.2 Hadoop Framework 4
9.1.3 Hadoop  Processes 6
9.2  Scheduling Algorithms 6
9.2.1 Dynamic Management of Hadoop clusters
 
9.2.2 Load Modeling  7
9.2.3 Scheduling Algorithm Design 8
9.2.4 Dynamic Scheduling algorithm 9
9.3 Energy Control System Design 10
9.3.1 The  System architecture 10
9.3.2 Detailed Design
9.4 Energy Efficient Scheduling for Multiple Users 13
9.5 Performance Evaluation 21
9.5.1 Test Environment 22
9.5 2. Test Results Analysis 23
9.6 Summary 28
Questions 29
References 29
 

Chapter 10 Cloud Workflow Management and Applications


10.1 Introduction Related research
10.2 Cloud Platform structured solutions
10.2.1 Requirements Analysis
10.2.2 Architecture
10.2.3 Swift Scientific Workflow Management System
10.2.4 Implementation Details
10.3  Cases Study
10.3.1 Modis image processing workflow
10.3.2  Application of network
10.4  Summary
References


Chapter 11  The Design and Application of Cloud Simulators
 

11.1 Introduction 3
11.2 Existing Simultors (Related Work) 3
11.2.1 CloudSim 3
11.2.1.1 CloudSim Introudciton 3
11.2.1.2 CloudSim Architecture 4
11.2.2 CloudAnalyst 6
11.2.2.1 CloudAnalyst Functions 6
11.2.2.2 CloudAnalyst Properties 6
11.2.2.3 Main Models and Components  of CloudAnalyst 7
11.2. 3 iCanCloud
11.3  The Architecture and Main Features  of CloudSched 9
11.3.1 Modeling Cloud Data Centers 10
11.3.2 Modeling Virtual Machines 11
11.3.3 Modeling Customer Requirements     12
11.4  Performance Metrics  7
11.4.1 Metrics for Multi-dimensional Load-balancing 8
11.4.2  Metrics for Energy-efficiency  10
11.4.3  Metrics for Maximizing Resource Utilization
11.5 Design and Implementation of CloudSched
11.5.1 IaaS Resources   11
11.5.2 Scheduling Process in Cloud Data Centers
11.5.3 Scheduling Algorithms
11.6  Performance Evaluation
11.6.1  Random Configuration of VMs and PMs
11.6.2  Divisible Size Configuration of PMs and VMs
11.6.3 Comparing Energy-Efficiency
11.7  Conclusion   16
References 17


Chapter 12 Summary and Outlook


12. 1  Conclusion on Resource Management and Scheduling
12. 2  Open Issues and Future Research Trends


Index

 


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