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系統識別號 U0026-2508201322444000
論文名稱(中文) 在雲端運算中基於基因演算法之虛擬機器資源整併方法
論文名稱(英文) A GA-Based Resource Consolidation Approach for Virtual Machines in Cloud Computing
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 101
學期 2
出版年 102
研究生(中文) 蔡禹婷
研究生(英文) Yu-Ting Tsai
學號 p76994741
學位類別 碩士
語文別 英文
論文頁數 54頁
口試委員 指導教授-郭耀煌
口試委員-陳培殷
口試委員-陳朝烈
口試委員-洪盟峰
中文關鍵字 資源整併  虛擬機遷移  雲端運算  基因演算法 
英文關鍵字 Resource consolidation  VM migration  Cloud computing  Genetic Algorithm 
學科別分類
中文摘要 在雲端計算中,基礎設施即服務(IaaS)是一個不斷增長的市場,使用戶能夠方便,按需求訪問雲端資源。透過虛擬化技術,IaaS可以提供使用者租用雲端計算資源和虛擬機(VM)。因為不同的虛擬機可能需要不同大小的資源,因此如何確定虛擬機映射的節點滿足虛擬機的資源需求在雲端環境中是一個重要的問題,必須有效地解決。我們將這種映射問題稱為虛擬機放置的問題(VMPP)。然而,虛擬機會依照應用程式的工作量提高或是減少所需要的資源。為了滿足虛擬機動態的需求,資源整併是不可缺少的。越來越多的研究課題從虛擬機管理延伸。
在這篇論文中,我們提出了一個兩階段方式進行資源整合,以最大限度地減少資源消耗。在第一階段,我們使用遺傳算法解決重新配置虛擬機放置節點的問題。第二階段,我們提出了一個機制,找到以最低花費的辦法遷移虛擬機,這樣可以減少節點的數量和整體遷移成本。
最後,實驗結果顯示,我們比現有的其他方法更有效地整合實體節點。
英文摘要 In cloud computing, infrastructure as a service (IaaS) is a growing market that enables users to access cloud resources in the convenient, on-demand manner. The IaaS can provide user to rent the resources of cloud computing and virtual machines (VMs) through virtualization technology. Because different VMs may demand different amounts of resources, an important problem that must be addressed effectively in the cloud is how to decide the mapping adaptively in order to satisfy the resource needs of VMs. The mapping problem is called virtual machine placement problem (VMPP). However, VM will change the requirement of resources according to the workload of application VM. Thus, it's necessary to apply resource consolidation technology to satisfy dynamically resource on demand.
In this thesis, we present a two-phase approach for resource consolidation to minimize resource consumption. In the first phase, we use a genetic algorithm to find an reconfiguration plan. In the second phase, we propose a mechanism to find a way to migrate VMs such that the number of active nodes and the overall migration cost could be minimized.
Finally, the experimental results show that we obtain well-consolidating active nodes than other existing approaches.
論文目次 LIST OF TABLES VIII
LIST OF FIGURES IX
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.1.1 Background of Cloud Computing and Virtual Machine 1
1.1.2 Issues of Resource Consolidation in Cloud Computing 4
1.2 CONTRIBUTION 5
1.2.1 Improving Problems 5
1.2.2 Overview of This Thesis’s Method and Technique 6
1.3 ORGANIZATION OF THIS THESIS 6
CHAPTER 2 RELATED WORK 7
2.1 VIRTUALIZED TECHNIQUE IN CLOUD 7
2.1.1 Virtualized Technique 7
2.1.2 The product of Virtualized Technique 8
2.2 RESOURCE CONSOLIDATION APPROACHES 8
2.2.1 Minimum the active nodes configuration 9
2.2.2 Minimum the overhead of migration 10
2.2.3 Resource Consolidation in cloud 11
2.3 GENETIC ALGORITHM IN CLOUD APPLICATION 11
2.3.1 Introduction of Genetic Algorithm 11
2.3.2 GA in Cloud Application 13
2.4 LIVE VIRTUAL MACHINE MIGRATION 14
CHAPTER 3 SYSTEM ARCHITECTURE OF RESOURCE CONSOLIDATION AND ELEMENTS 16
3.1 SYSTEM ARCHITECTURE FRAMEWORK 16
3.2 SYSTEM MODEL 19
3.2.1 Mathematical Model of System Profile 19
3.2.2 Modeling of Available Resources 21
Definition 3.1 : Weight Function 21
3.2.3 Modeling of Migration Cost Profile 22
Definition 3.2 : Migration Cost Function 22
3.3 A RESOURCE CONSOLIDATION APPROACH 23
3.3.1 System Flow Chart 23
3.3.2 The Virtual Machine Placement Policy 26
3.3.3 The Virtual Machine Migration Policy 34
CHAPTER 4 EXPERIMENTAL RESULTS 36
4.1 SIMULATION DESIGN 36
4.1.1 Experimental Environments and Setting 36
4.1.2 Experiment Parameters 37
4.2 EXPERIMENTAL RESULTS AND ANALYSES 38
CHAPTER 5 CONCLUSION AND FUTURE WORK 47
5.1 CONCLUSION 47
5.2 FUTURE WORK 48
REFERENCES 49
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