當(dāng)今通信和計(jì)算技術(shù)的迅速發(fā)展極大地改變了信息技術(shù)世界。這導(dǎo)致了一種新的計(jì)算范式的出現(xiàn),即云計(jì)算。雖然云計(jì)算提供了巨大的機(jī)遇,但它也給組織管理者（OMs）和決策者（DMs）帶來(lái)了各種挑戰(zhàn)。OMs/DMs在轉(zhuǎn)向云計(jì)算時(shí)面臨的首要挑戰(zhàn)是選擇最符合其組織需求的適當(dāng)云服務(wù)。這是一項(xiàng)對(duì)任何組織都具有深遠(yuǎn)影響的重大決定。在此之前,許多作者已經(jīng)提出了云服務(wù)排名和選擇（CSRS）問(wèn)題的各種解決方案。然而,現(xiàn)有的CSRS解決方案存在一致性、可靠性、復(fù)雜性等問(wèn)題。針對(duì)CSRS問(wèn)題的錯(cuò)綜復(fù)雜性和現(xiàn)有方法的不足,本文提出了創(chuàng)新的多準(zhǔn)則決策（MCDM）解決方案（方法/框架）,旨在幫助OMs/DMs在清晰和模糊的環(huán)境下做出明智的CSRS決策。本文對(duì)CSRS研究的突出貢獻(xiàn)和創(chuàng)新點(diǎn)如下:首先,我們提出了一個(gè)新的云代理框架,即服務(wù)選擇和推薦框架（SSRF）。與所有現(xiàn)有的方法/框架不同,SSRF涵蓋了 CSRS問(wèn)題的整個(gè)生命周期,最大限度地減少了對(duì)第三方的依賴,并提供了一種將體驗(yàn)質(zhì)量（QoE）和服務(wù)質(zhì)量（QoS）結(jié)合在CSRS決策中的機(jī)制。為了實(shí)現(xiàn)SSRF的服務(wù)評(píng)估/排序模塊,我們提出了一種新的CSRS綜合MCDM方法,旨... 

【文章來(lái)源】：大連理工大學(xué)遼寧省 211工程院校 985工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：212 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
Abstract
摘要
List of Acronyms and Abbreviations
1 Introduction
    1.1 Research Background
    1.2 Dissertation Objective and Questions
    1.3 Methodological Approaches and Theoretical Framework
    1.4 Significance of the Research
        1.4.1 Theoretical Significance
        1.4.2 Practical/Managerial Significance
    1.5 Conspicuous and Novel Contributions
    1.6 Organization/Structure of the Dissertation
2 Review of the Literature
    2.1 Contextual Knowledge of Cloud Computing
        2.1.1 Characteristics of Cloud Computing
        2.1.2 Cloud Computing Service Models/Layers
        2.1.3 Cloud Computing Deployment Models
    2.2 MCDM
    2.3 Overview of the CSRS Research
        2.3.1 MCDM-based Approaches for CSRS
        2.3.2 MODM-based Approaches for CSRS
        2.3.3 Other Approaches for CSRS
    2.4 Key Limitations, Open Issues, and Challenges
    2.5 Summary
3 A Novel Integrated MCDM Approach for Precise CSRS
    3.1 Chapter Highlights
    3.2 Introduction
    3.3 Proposed Framework: SSRF
    3.4 Proposed Integrated MCDM Approach
    3.5 Pseudocode for Implementation Algorithms
    3.6 Case Study
        3.6.1 Criteria for Evaluation of Cloud Storage Services
        3.6.2 Initial Scrutinization of Services and Criteria
        3.6.3 Data Collection for OC3S
        3.6.4 OC3S
    3.7 Comprehensive Analysis
        3.7.1 Comparative Analysis
        3.7.2 Sensitivity Analysis
    3.8 Summary
4 MOSS: Towards Consensual CSRS
    4.1 Chapter Highlights
    4.2 Introduction
    4.3 Proposed Methodology:MOSS
        4.3.1 Prequel (Stage 1)
        4.3.2 Assessment (Stage 2)
        4.3.3 Ranking of NDSS (Stage 3)
        4.3.4 Integrated Ranking (Stage 4)
        4.3.5 Consolidation/ Selection (Stage 5)
    4.4 Implementation/Expository Application of MOSS
        4.4.1 Contextual Information
        4.4.2 Prequel (Prequalification of Cloud Services)
        4.4.3 Assessment of Criteria
        4.4.4 Ranking of NDSS
        4.4.5 Integrated Ranking
        4.4.6 Consolidation/Optimal CSRS
    4.5 Comprehensive Analysis
        4.5.1 Comparative Analysis
        4.5.2 Complexity Analysis
    4.6 Summary
5 C3SF: CSRS Through a Broader Consensus
    5.1 Chapter Highlights
    5.2 Introduction
    5.3 Preliminaries
        5.3.1 TOPSIS Method
        5.3.2 VIKOR Method
        5.3.3 WSM
        5.3.4 WASPAS method
        5.3.5 Aggregation Methods
    5.4 Proposed Framework:C3SF
        5.4.1 Requirement Elicitation
        5.4.2 Scrutinization
        5.4.3 Evaluation
        5.4.4 Ranking/Selection
    5.5 Developing a Broader Consensus
    5.6 C3SF Pseudocode
    5.7 Implementation of C3SF
        5.7.1 Case Study Background
        5.7.2 CSRS for Case Company using C3SF
        5.7.3 Developing a Broader Consensus on Service Ranking
    5.8 Analysis and Discussion
        5.8.1 Sensitivity Analysis
        5.8.2 Suitability for Group Decision Making
        5.8.3 Brief Discussion
    5.9 Summary
6. CSRS under a Fuzzy Environment
    6.1 Chapter Highlights
    6.2 Introduction
    6.3 Preliminary Concepts
        6.3.1 Basic Definitions
        6.3.2 TFN Arithmetic Operations
    6.4 The Proposed FLBWM
        6.4.1 Transformation Rules for Linguistic Expressions
        6.4.2 Fuzzy Reference Comparison
        6.4.3 Steps of the FLBWM
    6.5 The Architecture of the Proposed CSSaaS Framework
        6.5.1 Monitoring&Indexing
        6.5.2 Filtration&Recommendation
    6.6 RecServ:An FLBWM Based Algorithm for CSRS/Recommendation
    6.7 Illustrative Applications for CSRS
        6.7.1 Application 1: High CPU Compute-Optimized CSRS
        6.7.2 Application 2: IaaS Selection
    6.8 Comparative Analysis
        6.8.1 Comparative Analysis of FLBWM with BWM
        6.8.2 Rank Conformance Analysis
        6.8.3 Rank Correlation Analysis
    6.9 Comprehensive Analysis
        6.9.1 Sensitivity Analysis
        6.9.2 Suitability for Collaborative Decision Making
        6.9.3 Suitability under Changes in Alternatives
        6.9.4 Uncertainty Management
    6.10 Summary
7 Conclusions
    7.1 Achievements and Managerial Implications
    7.2 Summary of Innovations
    7.3 Future Research and Developments
References
Research Publications during Ph.D. Period
Appendix A C3SF Case Study Code (Chapter 5)
Appendix B FLBWM Code (Chapter 6)
Appendix C Supplementary Example and Comparative Analysis of FLBWM
Acknowledgement
Curriculum Vitae


【參考文獻(xiàn)】：
期刊論文
[1]Qo S-Based Service Selection with Lightweight Description for Large-Scale Service-Oriented Internet of Things[J]. Chaocan Xiang,Panlong Yang,Xuangou Wu,Hong He,Shucheng Xiao.  Tsinghua Science and Technology. 2015(04)
[2]A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach[J]. Wen-Juan Fan,Shan-Lin Yang,Harry Perros,Jun Pei.  International Journal of Automation and Computing. 2015(02)



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