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研究生:張永君
研究生(外文):Chang, Yung Chun
論文名稱:在軟體定義儲存裝置上混合LRC和RAID5以降低能源消耗之研究
論文名稱(外文):The Study of Mixing LRC and RAID5 on Software-Defined Storage to Reduce Energy Consumption
指導教授:石維寬石維寬引用關係
指導教授(外文):Shih, Wei Kuan
學位類別:碩士
校院名稱:國立清華大學
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:2016
畢業學年度:104
語文別:中文
論文頁數:36
中文關鍵詞:軟體定義儲存節能雲端儲存資料容錯多層資料容錯
外文關鍵詞:Software Defined StorageEnergy-efficientCloud StorageData Fault toleranceMultilevel Data Fault Tolerance
相關次數:
  • 被引用被引用:0
  • 點閱點閱:168
  • 評分評分:
  • 下載下載:16
  • 收藏至我的研究室書目清單書目收藏:0
在數位化的時代,人們使用數位化的方式來儲存它們的資料在本地
或遠端的儲存設備上,隨著對於儲存要求的快速成長,傳統的儲存技術
開始不敷使用,因此,有著集中控制器的軟體定義儲存對於處理這些資
料要求而言,變成一個不錯的選擇,雖然目前有些研究和服務已經開始
利用軟體定義儲存所帶來的一些好處,但在這些研究和服務中,對於如
何建置多層資料容錯機制來去滿足不同階層的容錯需求上較少被提到,
除此之外,運用兩個或多個資料容錯機制在同一個硬碟組上會有一些問
題,且效能不好,為了去解決以上這些情況,本研究配合一些節能的方
法來去使多層資料容錯機制能夠建置在單一硬碟組中,一系列的實驗顯
示,跟原有架構相比,我們提出的方法能夠有效地降低儲存系統的能源
消耗。
In the era of digitalization, people store their data digitally on local or remote storage. With the rapid growth of storage demands, traditional storage techniques are considered inefficient. Therefore, Software Defined Storage (SDS) become a viable option since it includes a centralized controller to process data requirements. Although studies and services has been proposed and implemented to exploit the benefit bought by SDS, there is little discussion on how to enable multilevel data fault tolerance on SDS to satisfy different level of fault tolerance requirement. In addition, applying two or more data fault tolerance mechanisms on a same disk group could be problematic and not energy efficient. To address above issues, this study enables multilevel data fault tolerance on a single disk group with energy-efficient considerations. A series of experiments show that the proposed scheme could reduce the storage system energy consumption significantly when compared with the original architecture.
Abstract iii
Chapter 1. Introduction 1
Chapter 2. Background and Motivation 6
2.1 Software-Defined Storage 6
2.2 Data Fault-Tolerance Mechanism 8
2.3 Motivation 10
Chapter 3. Energy-Efficient Multilevel Data Fault Tolerance Design 14
3.1 Overview 14
3.2 Multilevel Data Fault Tolerance 17
3.3 Cache Policy – Hot Data Identification 20
3.4 Storage Volume Adjustment Policy 22
3.5 Energy-Efficient Data Placement Strategy 24
Chapter 4. Performance Evaluation 27
Chapter 5. Conclusion 32
References 34


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