Li Hao主編的《Banking Theory Based
Distributed Resource Management and
Scheduling for Hybrid Cloud Computing》從雲
計算的產生及發展變遷開始，逐步進入到混合雲計算
領域。針對混合雲計算由於其服務的不確定性和異構
環境，面臨著在服務提供、消費質量保證、定價及交
易風險預測等方面的挑戰。本書針對混合雲計算，研
究其模式下的資源交易核心問題。應用建立的雲銀行
模型中資源提供者、消費者及雲銀行代理三種角色在
計算資源及服務提供中面臨的問題及關鍵技術展開研
究。通過建立不同交易時期的定價策略，探索適合的
定價框架。對因服務不確定性在交易過程中產生的“
流動、信用”等交易風險進行分類、預測，並給出解
決方案。本書實驗環節采用基於開源的Eucalyptus，
OpenStack以及OpenNebula對所提出的方法進行測試
、驗證和比對。隨著混合雲計算研究的深入，這一研
究領域將有潛力改變真實環境下混合雲服務提供及消
費模式，為其交易過程提供有效的科學依據及實際運
用支撐。

1 Cloud Computing Resource Management and Scheduling Based on a Banking Model
1.1 Introduction
1.2 Inspiration for Computational Clouds
1.3 Banking Model Based Cloud Computing Resource Management and Scheduling
1.3.1 The Cloud Computing Requirements Analysis
1.3.2 The Technical Cloud Environment Requirement
1.4 Contributions to Research
1.4.1 Banking Model for Cloud Computing Resource Management
1.4.2 Optimal Deposit-loan Ratio
1.4.3 Risk Mitigation and Prediction Management
1.4.4 Pricing Scheme of the Cloud Resource over the Lifecycle
1.4.5 Cloud Computing Resource Management and Distribution Based on the Pareto Equilibrium
1.4.6 Building a Real Cloud Computing Platform Based on Open Sourcing
1.5 Organization
References
2 Background Study on Cloud Computing, Resource Management and Scheduling
2.1 Computational Clouds
2.2 The Evolution of Cloud Computing Technology
2.2.1 Parallel Computation
2.2.2 Distributed Computing
2.2.3 The Main Difference between Distributed Computing and Parallel Computing
2.2.4 Grid Computing
2.3 Cloud Computing
2.3.1 Background Research
2.3.2 The Definition of Cloud Computing
2.3.3 The Taxonomy of Cloud Computing
2.3.4 Internal Components of Cloud Computing
2.3.5 Main Differences between Cloud Computing and Grid Computing
2.4 Cloud Resource Management
2.4.1 Main Strategies of Cloud Resource Management
2.4.2 The Taxonomy of Cloud Resource Management
2.4.3 Key Cloud Resource Management Technology
2.5 Summary
References
3 Economics and Cloud Computing Resource Management
3.1 Survey of Economic Theories Based on the Grid Resource Management Project
3.2 Economic Theories can Provide a Solution to Solve Cloud Computing Resource Management Issues
3.2.1 Cloud Computing as a Business-driven Technology
3.2.2 Cloud Computing Technology Fit in with the Needs of the Society and Economic Laws
3.3 Using Economic Theories in Cloud Computing Resource Management
3.3.1 The Law of Demand and Supply in Cloud Computing Environment
3.3.2 The Law of Diminishing Marginal Returns in the Cloud Computing Environment
3.3.3 Monopolies in the Cloud Computing Environment
3.4 Summary
References
4 Problem Identification
4.1 Resource Accounting
4.2 Resource Scheduling
4.3 Cloud Computing Resource Transaction Risk Mitigation and Coping
4.4 The QoS Issue
References
5 Research Approaches to Banking Models for Cloud Computing Resource Management
5.1 Banking Model
5.2 How Does the Cloud Computing Follow the Real Bank to Do Transaction
5.2.1 Optimal Deposit-loan Ratio Theory in Cloud Banks
5.2.2 Identifying Factors Affecting the Cloud Bank
5.3 The Pricing Schema for Cloud Computing
5.4 Avoiding Banking Risk in the Transaction
5.5 Cloud Bank Scheduling and the Pareto Optimality
5.6 Interior Components of the Cloud Bank
5.7 Summary
References
6 Research Approaches for Risk Mitigation and Coping
6.1 The Risk Mitigation and Management in Commercial Banks
6.2 The Risk Mitigation in the Cloud Bank
6.2.1 The Classification of Risks in Cloud Bank
6.2.2 The Strategy of Risk Mitigation
6.2.3 The Strategy of the Risk Coping
6.3 Experiment Setup
6.4 Analysis of Experimental Results
6.5 Summary
References
7 Research Approaches for the Pricing Scheme of the Cloud Bank in the Price lifecycle
7.1 The Centralized Synchronous Algorithm
7.1.1 The Theory of Optimal Deposit-loan Algorithm
7.1.2 The Resource Management Model Based on Optimal Depositloan Algorithm
7.1.3 Single Resource Pricing Underlying the Cloud Bank Model
7.1.4 About the Optimal Deposit-loan Algorithm
7.2 Distributed Price Adjustment Algorithm
7.2.1 Pricing Scheme of Cloud Resources in the Initial Stage
7.2.2 Pricing Scheme of Cloud Resources in a Stable Stage
7.3 The Service Level Agreement of the Cloud Bank Model
7.3.1 Why We Need CBSLA
7.3.2 The CBSLA Framework
7.3.3 Signature Process of the CBSLA Contract
7.3.4 Generation of the CBSLA
7.4 Summary
References
8 Research Approaches for the Pareto Optimality Based Scheduling
8.1 The Concept of Pareto Optimality
8.1.1 Pareto Optimality
8.1.2 Pareto Improvement
8.2 Cloud Banks Achieve Optimal Resources Allocation by Pareto Theory
8.3 The Extended Pareto Optimality Model
8.3.1 Relative Proof
8.3.2 To Solve the Problem Under M x N Pareto Optimality
8.4 Cloud Banks Achieve Optimal Resources Allocation by Pareto Optimality Theory
8.5 Improvement of PO-based Allocation Strategy
8.6 The Steps of Dynamic Simulation
8.7 The Simulation Environment Set Up
8.8 Running the CloudSim Instance
8.9 Analysis of Experimental Results
8.10 Summary
References
9 The Real Laboratory Platform: IaaS Based Cloud Computing Platform
9.1 Introduction
9.2 Setting up the IaaS Based Cloud Computing Environment
9.2.1 The Comparison of the Two Kinds of the Platform Structures
9.2.2 Introduction of EUCALYPTUS
9.2.3 EUCALYPTUS Platform Advantage
9.2.4 EUCALYPTUS Platform Framework
9.2.5 EUCALYPTUS Components
9.2.6 EUCALYPTUS Configuration
9.2.7 EUCALYPTUS Installation Readiness
9.2.8 Installation of EUCALYPTUS Technical Route
9.2.9 Specific Methods of EUCALYPTUS Installation
9.3 Cloud Computing Simulator: CloudSim in Use
9.4 The Structure of the CloudSim
9.5 Summary
References
10 Conclusions and Future Directions
10.1 Summary
10.2 Conclusions
10.3 Future Directions
10.3.1 Supporting Accounting and Visualization
10.3.2 Supporting Complex Service and Task Description
10.3.3 Supporting Real Cloud Computing Environment Experiment Platform
10.3.4 Supporting a Variety of Risks in a Cloud Computing Environment