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研究生:李信璁
研究生(外文):Shin-Tsung Lee
論文名稱:顧客導向之雲端資源租賃聯合談判機制
論文名稱(外文):A NOVEL CUSTOMER-ORIENTED JOINT NEGOTIATION MECHANISM FOR CLOUD RESOURCES RENTING
指導教授:曾嘉影
指導教授(外文):Chia-Ying Tseng
口試委員:曾嘉影
口試委員(外文):Chia-Ying Tseng
口試日期:2015-07-03
學位類別:博士
校院名稱:大同大學
系所名稱:資訊工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:69
中文關鍵詞:衝突協議與談判賽局理論雲端資源管理決策支援系統服務品質
外文關鍵詞:Conflict Consultation and NegotiationGame TheoryCloud Resource ManagementDecision Support SystemsQuality of Service
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有別於分散式計算、網格計算和平行運算,雲端運算是近幾年相當熱門的議題,雲端運算的用戶僅需關心他們所請求的服務,而不用考慮與該服務有關的平台和基礎設施方面的問題。雖然,已有各種資源管理或節省資源之動態或靜態的配置方法,但資源供應商往往不會同時考慮用戶的服務品質(Quality of Service,QoS),其原因在於很難用單一的機制去滿足各種不同用戶的QoS。目前雲端資源供應市場主要由少數幾個大型的供應商所主導,但未來隨著技術的成熟將有更多類型的供應商可提供服務給用戶,如何增加現有客戶的忠誠度並有效且充分利用資源便成了雲端資源供應長期穩定獲利的最重要關鍵。本論文應用賽局理論建構顧客導向之雲端資源租賃決策協議機制,讓用戶可在不調整工作內容與時限的情境下與資源供應商進行議價,透過此機制不僅可讓用戶獲得額外之成本折扣,供應商亦可有更多的可用資源讓其它用戶來租用並同時提高資源額度的使用率。由模擬結果顯示,我們的協議機制產生的租用策略相較於各用戶原始方案來的更好,且資源供應商增加了25.93%的完整區段可供未來用戶來租用,雖然不同的工作負載情境可能會影響最終的利潤,但相較於用戶的歷史租用策略皆可產生更好的決策建議。
Cloud Computing is a popular topic in recent years, it is different from Distributed Computing, Grid Computing or Parallel Computing. The customers of cloud computing only pay their attentions to the services they requested, and they do not consider execution platform and infrastructure about their services. There are a variety of dynamic or static allocations of resources or energy savings about cloud resource management, but resource providers usually do not consider the user's quality of service (QoS), because it is difficult to use a single mechanism to satisfy all the different QoS requirements of users. The cloud resource supply markets may consist of few providers composed currently, but there may be more providers to provide services for the cloud users in the future. How to increase the loyalty of original users and attract new users to rent is a very important key to maintain the benefit of cloud resources' supply. In our research, we applied game theory to construct a customer-oriented joint negotiation mechanism of cloud resource renting for decision-making, such that the cloud users can negotiate with resource providers without adjust the content of the work and the constraints of deadline. By applying the proposed mechanism, the final results of negotiation can let users to obtain extra remuneration and allow resource providers to have more complete resource section for new users to rent. In our simulation, the results of negotiation for all users compared to the original solutions obtain the better recommendation and increase resource providers 25.93% of the full time sections for new users to rent. Although the context of different workloads could bring the different benefit, but the final decision recommendations provided can produce better rental decisions compared to the pre-existing history of rental record for cloud environment.
CHAPTER 1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Purposes 2
1.3 Research Scope and Limitations 4
1.4 Research Methods and Content 4
1.5 Research Process and Architecture 5
CHAPTER 2 LITERATURE REVIEW 6
2.1 Conflict Resolution 6
2.2 Game Theory 8
2.3 Cloud Computing 14
2.3.1 Virtualization Technology 16
2.3.2 Virtual Machine Migration 17
2.3.3 Quality of Service 18
2.3.4 Cloud Resource Management 18
2.4 Decision Support System 20
CHAPTER 3 CUSTOMER-ORIENTED MECHANISM IN CLOUD RESOURCE RENTING 22
3.1 Situation Analysis of Cloud Resource Renting 22
3.2 Strategies Agreements of Users and Providers 22
3.3 Benefit Analysis of Cloud Resources 23
3.4 Decomposition of Collision between Users and Providers 24
3.5 Design of the Cloud Resource Rental Resolution Mechanism 26
CHAPTER 4 EVALUATION AND ANALYSIS OF SIMULATION 33
4.1 Negotiation by Cloud Users and Resource Providers 33
4.2 Scenarios in Cloud Resource Renting Simulation 35
4.3 Negotiation Mechanism Design 36
4.4 Model of the Cloud Resource Renting Negotiation Game 37
4.5 Simulation Results and Analysis 40
4.6 Results of Assessment and Discussion 58
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 62
5.1 Conclusions and Contributions 62
5.2 Possible Future Research Directions 63
REFERENCES 64
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