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研究生:陳廷肇
研究生(外文):Ting-Chao Chen
論文名稱:以基因演算法針對雲端運算環境虛擬機器重分配機制之研究
論文名稱(外文):A Genetic Algorithm for Virtual Machines Re-provisioning in a Cloud Computing Environment
指導教授:朱正忠朱正忠引用關係
指導教授(外文):Cheng-Chung Chu
口試委員:王豐堅蔡清欉盧志偉陳瑞男朱正忠
口試委員(外文):Feng-Jian WangChing-Tsorng TsaiChih-Wei LuJuei-Nan ChenCheng-Chung Chu
口試日期:2012-06-29
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊工程學系
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:2012
畢業學年度:100
語文別:中文
論文頁數:65
中文關鍵詞:綠色雲端運算基因演算法重分配
外文關鍵詞:Green Cloud ComputingGenetic AlgorithmRe-provisioning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:261
  • 評分評分:
  • 下載下載:23
  • 收藏至我的研究室書目清單書目收藏:2
近年由於雲端運算的興起,各家企業紛紛將服務從用戶端改為雲端服務模式,但是在利用虛擬化技術的同時,雲端伺服器叢集也因為提供眾多的服務而消耗大量的能源,造成雲端供應商營運成本的增加與降低基礎建設的投資報酬率,更增加了碳的排放量而衝擊環境,因此,我們需要一個更好的解決方案來提升能源的使用效率。
透過重新安排虛擬機器的配置以減少實體機器數量可以達到節約能源、提升能源使用效率的目的,然而,如何將虛擬機器分配到最少量的實體機器以達到最少的能源消耗是一個裝箱問題。
為了解決這個NP-hard問題,本研究中提出一個以基因演算法為基礎,調整了一般用於分群問題的染色體編碼方式,降低其運算時的時間複雜度,使其更適合應用在雲端環境中;透過事先的分群,掌握虛擬機器的資料分布,簡化運算時的迴圈數量;並考慮最小化實體機器的數量時虛擬機器在各實體機器間傳輸的搬移成本、以及在使用者需求與服務層級協議對於服務品質的限制下,動態地改變虛擬機器的分配方式,以達到更佳的能源使用效率。
最後,利用雲端模擬器CloudSim進行模擬實驗,並且與其他方法進行比較及分析,驗證本方法的優勢與可用性。
Recently, due to the rise of cloud computing technologies, more and more enterprises change their service type from end-user to cloud services model. Cloud centers consume a large amount of energy because of providing numerous services while using the virtualization techniques. These cause the increase of business cost and lower return on investment of infrastructures of cloud services providers, and even an increment of carbon emission. Therefore, we need a better solution to improve the efficiency of energy use.
Re-provisioning of virtual machines to decreasing the quantity of physical machines may reach the goal of energy saving and increasing the energy utility efficiency. However, to provision virtual machines to the least amount of physical machines in order to achieve the minimum energy consumption is a bin-packing problem.
In order to solve this NP-hard problem, a method based on genetic algorithm and adjusting the chromosome encoding from a general method for clustering problems to lower the time complexity during processing and make it more suitable for applications in a cloud environment is proposed in this paper. In addition, consider minimizing the number of physical machines, migration costs of virtual machines re-provisioning between the entities of each machine, and the constraints of quality of service from user requirements and service level agreements to dynamically re-provision virtual machines to achieve better energy efficiency.
Finally, using the cloud environment simulator named CloudSim to compare and analyze with other methods to verify the advantages and availability of this method.
目錄
摘要 iii
Abstract iv
致謝 v
目錄 vi
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1. 前言 1
1.2. 研究動機 2
1.3. 研究目的 3
1.4. 章節安排 4
第二章 背景知識與相關研究 5
2.1. 分群問題 5
2.2. K-means 6
2.3. 基因演算法 8
2.4. 關係基因演算法 12
2.5. CloudSim 15
2.6. 雲端環境節能研究 17
第三章 研究方法 24
3.1. GA4VMR系統流程 24
3.2. 系統模型 25
3.3. 數學模型 26
3.3.1. 定義一:CPU部分 27
3.3.2. 定義二:資料傳輸部分 27
3.3.3. 定義三:實體機器負荷部分 28
3.4. 初始化階段 29
3.4.1. 基因之染色體編碼 29
3.4.2. 分群 31
3.5. 參數設定 32
3.5.1. 族群數 33
3.5.2. 適應性函數 36
3.5.3. 交配機率 37
第四章 實驗與分析 40
4.1. 族群數設定分析 40
4.2. 交配機率設定分析 42
4.3. 實驗結果 43
4.4. 評估與比較 46
第五章 結論與未來工作 49
參考文獻 51


參考文獻
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