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研究生: 張家瑋
Chang, Chia-Wei
論文名稱: 基於雲端電腦系統實際負載之效能評估及模型化
Performance Evaluation and Modelling based on Empirical Data Collection in Cloud Computing System
指導教授: 李忠憲
Li, Jhong-Sian
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 英文
論文頁數: 52
中文關鍵詞: 雲端運算資源分配雲端運算環境
外文關鍵詞: Cloud Computing, Resource Allocation, Cloud Computing Environment
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  • 近來雲端運算提供給應用程式提供者一個很好的平台,再加上它還運用了虛擬化的技術;在使用者方面,他們可以在遠端使用資源。而在提供資源的這一方,他們在資源管理和建立起基礎環境的複雜度也降低了。在我的研究中,著重在雲端環境每台虛擬機器特性的探討,在執行每次使用者要求時,管理者必須去藉由一些管理系統去了解目前每台虛擬機器的狀況。 藉由我的研究,資源管理者可以輕鬆的了解每一台虛擬機器的特性,這樣雲上面的每一份資源都可以有效的被利用。

    Recently, cloud computing has provided a well platform for application provider and it uses the virtualization. For user, they enable to control the remote resource. Moreover, for provider side, cloud computing has improved the effect on resource management as well as to achieve the cloud environment also becomes simple. In my research, I focus on understand the feature of each VMs and model them. When the requirement comes, resource supervisors have to check the condition of VMs by some supervised systems. According to my research, the resource supervisors can easily to understand each VMs feature so the resource of cloud environment would use effectively.

    Contents 摘要 I ABSTRACT II 誌謝 III CONTENTS IV LIST OF TABLES VI LIST OF FIGURES VII CHAPTER 1 INTRODUCTION 8 1.1 INTRODUCTION 8 1.2 MOTIVATION 9 1.3 CONTRIBUTION 10 1.4 ORGANIZATION 10 CHAPTER 2 BACKGROUND & RELATED WORK 11 2.1 CLOUD COMPUTING 11 2.2 THE RESOURCE MANAGEMENT IN CLOUD COMPUTING 13 2.3 THE DATA COLLECTION 14 2.4 STATISTICAL METHODS 18 2.4.1 Hurst Parameter 18 2.4.2 Markov Process 20 CHAPTER 3 VIRTUAL MACHINE ALLOCATION PROBLEM 22 3.1 DEFINITION AND ASSUMPTION 22 3.2 SCENARIO DESCRIPTION 23 3.2.1 Virtual Machine Statistic Problem 23 3.3 PROPOSED METHOD 24 3.3.1 Assumption (collecting Real Data form VMware vSphere) 24 3.3.2 Using Beta Distribution as CPU Usage Module 27 3.3.3 Hurst Parameter and Markov Process as CPU Module 29 CHAPTER 4 PERFORMANCE EVALUATION 35 4.1 PROFILING WITH EACH VM FEATURE 35 4.1.1 Profiling with Hurst Parameter 35 4.1.2 Profiling with Markov Transition Matrix 41 4.2 DISCUSSION WITH THE MARKOV TRANSITION MATRIX AND HURST PARAMETER 47 CHAPTER 5 CONCLUSION AND FUTURE WORK 48 5.1 DISCUSSION AND FUTURE WORK 48 REFERENCES 50 APPENDIX 52

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    [9] The guide of vSphere resource management
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