基于银行模型的混合云计算
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广东广州
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作者Li Hao 编著
出版社科学出版社
ISBN9787030523761
出版时间2017-01
装帧平装
开本16开
定价78元
货号1201497712
上书时间2024-08-09
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目录
1Cloud Computing Resource Management and Scheduling Based on a Banking Model
1.1Introduction
1.2Inspiration for Computational Clouds
1.3Banking Model Based Cloud Computing Resource Management and Scheduling
1.3.1The Cloud Computing Requirements Analysis
1.3.2The Technical Cloud Environment Requirement
1.4Contributions to Research
1.4.1Banking Model for Cloud Computing Resource Management
1.4.2Optimal Deposit-loan Ratio
1.4.3Risk Mitigation and Prediction Management
1.4.4Pricing Scheme of the Cloud Resource over the Lifecycle
1.4.5Cloud Computing Resource Management and Distribution Based on the Pareto Equilibrium
1.4.6Building a Real Cloud Computing Platform Based on Open Sourcing
1.5Organization
References
2Background Study on Cloud Computing, Resource Management and Scheduling
2.1Computational Clouds
2.2The Evolution of Cloud Computing Technology
2.2.1Parallel Computation
2.2.2Distributed Computing
2.2.3The Main Difference between Distributed Computing and Parallel Computing
2.2.4Grid Computing
2.3Cloud Computing
2.3.1Background Research
2.3.2The Definition of Cloud Computing
2.3.3The Taxonomy of Cloud Computing
2.3.4Internal Components of Cloud Computing
2.3.5Main Differences between Cloud Computing and Grid Computing
2.4Cloud Resource Management
2.4.1Main Strategies of Cloud Resource Management
2.4.2The Taxonomy of Cloud Resource Management
2.4.3Key Cloud Resource Management Technology
2.5Summary
References
3Economics and Cloud Computing Resource Management
3.1Survey of Economic Theories Based on the Grid Resource Management Project
3.2Economic Theories can Provide a Solution to Solve Cloud Computing Resource Management Issues
3.2.1Cloud Computing as a Business-driven Technology
3.2.2Cloud Computing Technology Fit in with the Needs of the Society and Economic Laws
3.3Using Economic Theories in Cloud Computing Resource Management
3.3.1The Law of Demand and Supply in Cloud Computing Environment
3.3.2The Law of Diminishing Marginal Returns in the Cloud Computing Environment
3.3.3Monopolies in the Cloud Computing Environment
3.4Summary
References
4Problem Identification
4.1Resource Accounting
4.2Resource Scheduling
4.3Cloud Computing Resource Transaction Risk Mitigation and Coping
4.4The QoS Issue
References
5Research Approaches to Banking Models for Cloud Computing Resource Management
5.1Banking Model
5.2How Does the Cloud Computing Follow the Real Bank to Do Transaction
5.2.1Optimal Deposit-loan Ratio Theory in Cloud Banks
5.2.2Identifying Factors Affecting the Cloud Bank
5.3The Pricing Schema for Cloud Computing
5.4Avoiding Banking Risk in the Transaction
5.5Cloud Bank Scheduling and the Pareto Optimality
5.6Interior Components of the Cloud Bank
5.7Summary
References
6Research Approaches for Risk Mitigation and Coping
6.1The Risk Mitigation and Management in Commer Banks
6.2The Risk Mitigation in the Cloud Bank
6.2.1The Classification of Risks in Cloud Bank
6.2.2The Strategy of Risk Mitigation
6.2.3The Strategy of the Risk Coping
6.3Experiment Setup
6.4Analysis of Experimental Results
6.5Summary
References
7Research Approaches for the Pricing Scheme of the Cloud Bank in the Price lifecycle
7.1The Centralized Synchronous Algorithm
7.1.1The Theory of Optimal Deposit-loan Algorithm
7.1.2The Resource Management Model Based on Optimal Depositloan Algorithm
7.1.3Single Resource Pricing Underlying the Cloud Bank Model
7.1.4About the Optimal Deposit-loan Algorithm
7.2Distributed Price Adjustment Algorithm
7.2.1Pricing Scheme of Cloud Resources in the Initial Stage
7.2.2Pricing Scheme of Cloud Resources in a Stable Stage
7.3The Service Level Agreement of the Cloud Bank Model
7.3.1Why We Need CBSLA
7.3.2The CBSLA Framework
7.3.3Signature Process of the CBSLA Contract
7.3.4Generation of the CBSLA
7.4Summary
References
8Research Approaches for the Pareto Optimality Based Scheduling
8.1The Concept of Pareto Optimality
8.1.1Pareto Optimality
8.1.2Pareto Improvement
8.2Cloud Banks Achieve Optimal Resources Allocation by Pareto Theory
8.3The Extended Pareto Optimality Model
8.3.1Relative Proof
8.3.2To Solve the Problem Under M x N Pareto Optimality
8.4Cloud Banks Achieve Optimal Resources Allocation by Pareto Optimality Theory
8.5Improvement of PO-based Allocation Strategy
8.6The Steps of Dynamic Simulation
8.7The Simulation Environment Set Up
8.8Running the CloudSim Instance
8.9Analysis of Experimental Results
8.10Summary
References
9The Real Laboratory Platform: IaaS Based Cloud Computing Platform
9.1Introduction
9.2Setting up the IaaS Based Cloud Computing Environment
9.2.1The Comparison of the Two Kinds of the Platform Structures
9.2.2Introduction of EUCALYPTUS
9.2.3EUCALYPTUS Platform Advantage
9.2.4EUCALYPTUS Platform Framework
9.2.5EUCALYPTUS Components
9.2.6EUCALYPTUS Configuration
9.2.7EUCALYPTUS Installation Readiness
9.2.8Installation of EUCALYPTUS Technical Route
9.2.9Specific Methods of EUCALYPTUS Installation
9.3Cloud Computing Simulator: CloudSim in Use
9.4The Structure of the CloudSim
9.5Summary
References
10Conclusions and Future Directions
10.1Summary
10.2Conclusions
10.3Future Directions
10.3.1Supporting Accounting and Visualization
10.3.2Supporting Complex Service and Task Description
10.3.3Supporting Real Cloud Computing Environment Experiment Platform
10.3.4Supporting a Variety of Risks in a Cloud Computing Environment
内容摘要
本书从云计算的产生及发展变迁开始，逐步进入到混合云计算领域。针对混合云计算由于其服务的不确定性和异构环境，面临着在服务提供、消费质量保证、定价及交易风险预测等方面的挑战。本书针对混合云计算，研究其模式下的资源交易核心问题。应用建立的云银行模型中资源提供者、消费者及云银行代理三种角色在计算资源及服务提供中面临的问题及关键技术展开研究。通过建立不同交易时期的定价策略，探索适合的定价框架。对因服务不确定性在交易过程中产生的"流动、信用"等交易风险进行分类、预测，并给出解决方案。本书实验环节采用基于开源的Eucalyptus，OpenStack以及OpenNebula对所提出的方法进行测试、验证和比对。随着混合云计算研究的深入，这一研究领域将有潜力改变真实环境下混合云服务提供及消费模式，为其交易过程提供有效的科学依据及实际运用支撑。
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