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研究生:林韋存
研究生(外文):Wei-Tsun Lin
論文名稱:建構儲存體容量被動遷徙政策於生命週期管理系統之研究
論文名稱(外文):Construct Storages Capacity Migration Policy for Information Lifecycle Management System
指導教授:蔡孟峰蔡孟峰引用關係
指導教授(外文):Meng-Feng Tsai
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:69
中文關鍵詞:資訊生命週期管理
外文關鍵詞:Information Lifecycle Management
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資訊生命週期管理(Information Lifecycle Management, ILM)也就是一群儲存廠商所提供給資訊管理經理人的軟體和服務,以協助其將儲存建置所花費的成本,更能反應在資料的可用性及記錄管理(records management, RM) 規範上。ILM 軟體解決方案,是為改善企業資訊資產從產生的那一刻起至最終端的管理問題。資訊生命週期管理主要利用各種政策來管理資訊系統,本論文主要研究建構資訊紀錄儲存體配置初始化政策和被動式容量觸發資訊週期遷政策來進行資訊週期的管理,並利用0/1背包問題演算法來滿足最低系統回應要求和最大儲存體使用率。並且撰寫了一支根據論文論點的程式進行分析,進行相關系統參數設定方式的討論。希望論文研究可以給予在生命週期管理中加入降低系統平均存取回應及增進儲存體使用率演算法,使系統資源被有效及正確的利用和降低資訊系統建構成本。
Information Lifecycle Management (ILM) is comprised of policies, processes, practices, and tools used to align the business value of information with the most ap-propriate and cost effective IT infrastructure from the time information is conceived through its final disposition. This paper describes the research on ILM system initia-tion and storage capacity migration policy. The research includes system storages utility rate increase and storages access performance enhancement. Our ILM system policies rule apply trigger functions in active database system, so we used the tech-nique of active database to construct ILM system policies. In order to enhance ILM system storages access performance and reduce total storage devices cost, we use the algorithm of the 0/1 knapsack problem to construct ILM system policies. An experi-ment program is implemented to verify our methodology’s accuracy and efficiency. And we will analyze and discuss how to set the arguments of ILM system policies rule.
Chinese Abstract...i
English Abstract...iii
List of Tables...vii
List of Figures...viii
Chapter 1 Introduction....- 1 -
1.1 Introductions of ILM and TCO...- 1 -
1.2 Research motivation and objective...3 -
1.3 The tactic of our research...- 4 -
Chapter 2 Related Works...- 5 -
2.1 Introduction of other existing system...- 5 -
2.2 Active database...- 5 -
2.3Application physical system structure...- 6 -
2.4Application field and future work...- 7 -
Chapter 3 Background procedure and methodologies...- 9 -
3.1 Policy-Based Information Lifecycle Management in a large-scale file...- 9 -
3.2 Policies for Lifecycle Management...- 9 -
3.3 ILM policies model...- 12 -
3.4 Policy rules decomposition...- 13 -
3.5 The 0/1 knapsack problem...- 16 -
3.6 Algorithms for 0/1 knapsack problem...- 17 -
Chapter 4 ILM Capacity migration policies...- 24 -
4.1 System workload estimation...- 24 -
4.2 Reconfigure existence system and file placement rule processing...- 27 -
4.3 Capacity migration policy rule...- 30 -
4.3.1 Migration policy for performance...- 31 -
4.3.2 Migration policy for overflow...- 36 -
4.4 The migration rule cost and migration rule benefit...- 38 -
Chapter 5 Experimental framework and procedure...- 40 -
5.1 Experiment program input and output...- 40 -
5.2 File Element Location Matrix...- 40 -
5.3 Policy rule operating procedure and method...- 42 -
Chapter 6 Experiment result...- 45-
6.1 Experiment environment argument...- 45 -
6.2 Results data analysis...- 46 -
6.3 Experiment program promotion...- 54 -
Chapter 7 Conclusion...- 55 -
Reference...- 56 -
[1]Lisa M. Ellram, Total cost of ownership - An analysis approach for purchasing, Arizona State University, Tempe, Arizona (1)2-3
[2]Wikipedia, http://en.wikipedia.org/wiki/Knapsack_problem
[3]Carlo Zaniolo, Active database rules with transaction-conscious sta-ble-model semantics, Computer Science Department, University of California Los Angeles, California 90024. (3)15-16
[4]Jennifer Widom , The starburst active database rule system, Deport-ment of Computer Science Stanford University Stanford, CA 94305-2140. (4)7-8
[5]Mandis Beigi,1 Murthy Devarakonda, Rohit Jain, Marc Kap-lan,David Pease, Jim Rubas, Upendra Sharma, and Akshat Verma: Policy-based information lifecycle management in a large-scale file system ,IBM Research, P.O. Box 704, Yorktown Heights, NY POL-ICY 2005: 139-148 (3)3-5, (4)6-7,
[6]Norman W. Paton, Oscar Díaz. Active database systems. March 1999 ACM Computing Surveys (CSUR), (1)2-3
[7]Oscar H. Ibarra , Chul E. Kim: Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems Journal of the ACM (JACM) Volume 22 , Issue 4 (October 1975) Pages: 463 - 468 Year of Publication: 1975 ISSN:0004-5411
[8]Sartaj, Sahni: Approximate Algorithms for the 0/1 Knapsack Problem Journal of the ACM (JACM) archive Volume 22 , Issue 1 (January 1975) table of contents Pages: 115 - 124 Year of Publication: 1975 ISSN:0004-5411
[9]Information Lifecycle Management From major medical clinics to Major League Baseball –find out how ILM is benefiting all types of organizations, and how it can benefit you. A Business Brief January 2005, 1-4
[10]EMC Storage Management ControlCenter Family: Integrated and Heterogeneous Storage Resource Management Allen Jiaan Technical Consultant EMC Taiwan, 23-30
[11]Allan Saunders Product Marketing Director Oracle Corporation: ILM A Database Perspective, 2-4
[12]David Reiner, Gil Press, Mike Lenaghan, David Barta EMC Corpo-ration, Rich Urmston Legato Systems: Information Lifecycle Man-agement: The EMC Perspective Proceedings of the 20th International Conference on Data Engineering (ICDE’04) 1063-6382/04 © 2004 IEEE
[13]Damian Marriott, Morris Sloman: Management Policy Service for Distributed Systems Imperial College, Deparfment of Computing, 180 Queen’s Gate, London, SW7 282, UK.
[14]G¨unther R. Raidl: An Improved Genetic Algorithm for the Multi-constrained 0–1 Knapsack Problem
[15]Richard Neapolitan and Kumarss Naimipour: Foundations of Algo-rithms (Using C++ pseudo code) second edition, (4)178-184
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