簡易檢索 / 詳目顯示

研究生: 余坤庭
Yu KunTyng
論文名稱: 以資料探勘進行視訊系統快取機制之研究
A Cache Mechanism Research of Video-On-Demand System by using Data Mining
指導教授: 石維寬教授
Dr. Wei Kuan Shih
口試委員:
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2004
畢業學年度: 92
語文別: 中文
論文頁數: 60
中文關鍵詞: 隨選視訊系統快取機制資料探勘命中率
外文關鍵詞: VOD, Cache Mechanism, Data Mining, hit rate
相關次數: 點閱:2下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 近年來由於網路技術不斷地進步及網路頻寬大幅地增加,過去只能提供簡單的文字及聲音的網路媒體,如今進化為能提供圖片及多媒體影片的網路媒體。例如:由中華電信公司推出的互動式多媒體隨選視訊服務(MOD)就是一個最典型的例子。由於網路使用人口呈現越來越多的趨勢,且網路的應用也越來越往多媒體影片服務方面來發展。有鑑於此,本論文將利用資料探勘的概念,配合預存式機制的技術來解決在視訊系統中的影片快取機制問題。配合該機制,我們能提供一個簡單的小型配送系統,來解決目前在多媒體配送系統中,提高快取伺服器的命中率減少點選影片的反應時間及大量的人力資源浪費還有避免人為疏失上的可能。
    本論文的核心在探討在視訊系統(如VOD、MOD系統)環境下快取機制的資料探勘(data mining)議題。由於視訊系統都有網路頻寬的限制問題,所以將欲觀賞的影片資料先存放於當地的視訊伺服器主機內,來提升用戶連線的反應時間效率及減少核心網路流量是有必要的。我們利用資料探勘的技術,從媒體中心的影片資料庫中找出一般使用者可能欲觀看的影片,預先將要觀賞的影片存入當地的視訊伺服器(或稱快取伺服器)的儲存空間內,使得使用者觀看影片的反應時間有大幅度的改善。
    本論文第四章以模擬方式對本研究提出系統架構作效能評估,發現影片命中率有顯著改善,進而改善整體視訊系統的效能。本論文提出的PSM機制與LFU、LRU演算法相比較,在命中率上有明顯的提升。在快取空間佔影片資料庫比重為三成時,比LFU及LRU的快取命中率平均提升26.6%。
    本論文研究建立視訊系統中TVOD的快取機制,讓使用者可以更有效率的在TVOD系統中觀看影片。藉此提供未來TVOD系統設計者更完善的考量,作為日後發展出更完美的TVOD視訊系統管理機制的參考。


    Recently, by reason of progressing of the internet technology and high-speed network bandwith, it will come true to provide services of video-on-demand on internet. Now, Internet not only provides text and picture medias but also videoes.For a typical example, Chunghwa Telecom‘s MOD service. Because more people using internet than before, application of internet begins toward multimedia services field. Because of this, we use the technology of data mining and PreStore Mechanism (PSM) to help solving cache problem in VOD (video-on-demand) system.Using PSM, we can provide a simple and small video delivery system. It can help us reducing response time of waiting and plenty of human resources. Finally, it can also help us avoiding human fault on VOD delivery system.
    This thesis discusses the issue of cache mechanism of VOD/MOD system. Because the limit of internet bandwith, it is always necessary for VOD system putting hot moive on local video server/cache video server. In this case, VOD system will have good reponse time and reducing instant flush of network flow.We use data mining method to find out hot movies which general users maybe want to see. We also use data mining skill to prestore hot movies in local video server/cache video server. That did work for reponse time of waiting to seeing movies.
    The chapter 4 of this thesis provides simulation methods to analysis performance of VOD system.We find reponse time and performance of VOD system having massive progress. We also compare our PSM to LFU and LRU. We find the results of PSM’s hit rate better than LFU and LRU. The improvement on LRU and LFU for hit ratio is 26.6% averagely.
    The study establishes the cache mechanism in TVOD environment. Users can query the movie more efficiently on the TVOD system. It provides the TVOD system makers with a more perfect decision environment and services as a further reference for the development of an advanced management mechanism of TVOD system.

    摘要 2 英文摘要 3 誌謝 4 目錄 5 圖目錄 6 表目錄 7 第一章 緒論 8 1.1研究背景 8 1.2相關技術 10 1.3研究動機 12 1.4問題描述 16 1.5 論文架構 18 第二章 相關文獻探討 19 2.1隨選視訊系統VOD相關研究 19 2.2快取機制相關研究 20 2.3資料探勘 23 第三章 系統設計 28 3.1架構簡介 28 3.2系統運作流程 30 3.3預存式機制PSM 31 第四章 實驗結果分析 44 4.1模擬模型 44 4.2實驗假設 46 4.3實驗結果 47 第五章 結論 55 5.1結論 55 5.2未來工作 55 參考文獻 56

    [1]財團法人台灣網路資訊中心。台灣網路資訊中心網路使用調查。
    上網日期:民93年6月2日。http://www.twnic.net.tw/download/200307/200307index.shtml
    [2]中國互聯網絡信息中心。中国互联网络发展状况统计调查。上網日期:民93年6月2日。http://www.cnnic.net.cn/index/0E/00/11/index.htm
    [3]Brain D. Davison; “A Web Caching Primer;” IEEE Internet Computing,
    Volume 5, Number 4; IEEE; p.38-45 ; 2001.
    [4]David W. Cheung, Ben Kao, Joseph Lee; “Discovering user access
    patterns on the World Wide Web;” Knowledge Based Systems Journal;
    Elsevier Science , V10, N6; 1998.
    [5]Evangelos Markatos , Catherine E. Chronaki ; “A Top-10 Approach to
    Prefetching on the Web;” In Proceedings of the INET 98 Conference; July
    1998.
    [6]Jinquan Li, Z.X. Wang, Daniel Zeng, Fei-Yue Wang; “Combined
    Coherence and Prefetching Mechanisms for Effective Web Caching;” 2001
    IEEE International Conference on , Vol. 5 ; IEEE; p.3034-3038; 2001.
    [7]美國nSTREAMS Technologies, Inc.公司。上網日期:民93年6月2日。http://www.nstreams.com.
    [8] H. Shachnai and P. S. Yu, “The role of wait tolerance in effective batching: A paradigm for multimedia scheduling schemes,” Technical Report RC 20038, IBM Research Division, T.J. Watson Research Center, April 1995.
    [9]Dan, A., Sitaram, D., and Shahabuddin, P., “Scheduling policies for an
    on-demand video server with batching,” Proceedings of ACM Multimedia,
    pp. 15-23, 1994.
    [10] Dan, A., Shahabuddin, P., Sitaram, D., and Towsley, D., “Channel allocation under batching and VCR control in video-on-demand systems,” Journal of Parallel and Distributed Computing, vol. 30, no. 2, pp. 168-179, November 1995.
    [11] Dan, A., Sitaram, D., and Shahabuddin, P., “Dynamic batching policies for an on-demand video server,” Multimedia Systems, vol. 4, no. 3, pp. 112-121,June 1996.
    [12] Carter, S. W. and Long, D. D. E., “Improving video-on-demand server
    efficiency through stream tapping,” Proceedings of the Sixth International
    Conference on Computer communications and Networks, pp. 200-207, Las
    Vegas, N.V., U.S.A., September 1997.
    [13]Aggarwal, C. C., Wolf, J. L., and Yu, P. S., “A permutation-based pyramid broadcasting scheme for video-on-demand systems,” IEEE Proceedings of the International Conference on Multimedia Computing and Systems, pp.118-126, June 1996.
    [14]Chiueh, T. and Lu, C., “A periodic broadcasting approach to
    video-on-demand service,” International Society for Optical Engineering,
    vol. 26, no. 15, pp. 162-169, October 1995.
    [15]Gao, L., Kurose, J., and Towsley, D., “Efficient schemes for broadcasting popular videos,” International Workshop on Network and Operating Systems Support for Digital Audio and Video, pp. 317-329, August 1998.
    [16] Juhn, L. S. and Tseng, L. M., “Fast broadcasting for hot video access,” Real-Time Computing Systems and Applications, pp. 237-243, October 1997.
    [17] H. Shachnai and P. S. Yu, "Exploring wait tolerance in effective batching for video-on-demand scheduling," in Proc. International Conference on ComputerSystems and Software Engineering, pp. 67-76, 1997.
    [18] Chen, M. S., Han, J., & Yu, P.S. (1996). Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883.
    [19] Fong, J., Wong, H.K., & Huang, S.M. (2003). Continuous and incremental data mining association rules using frame metadata model. Knowledge-Based System, 16(2), 91-100.
    [20] Fu, Y. (1997). Data mining Tasks, techniques and applications, IEEE
    Potentials, 16(4), 18-20.
    [21] Cheung, D., Han, J., Ng, V., & Wong, C. Y. (1996). Maintenance of
    discovered association rules in large databases: An incremental updating
    technique. Proceedings of 1996 International Conference on Data Engineering, 106-114.
    [22] Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules. Proc. of the 20th Int’l Conference on Very Large Databases, 487-499.
    [23] D. J. Gemmell, H. M. Vin, D. D. Kandlur, and L. A. Rowe, “Multimedia storage servers: a tutorial,” Computer, Vol. 28, No. 5, 1995, pp. 40-49.
    [24] Buretta, M. (1997). Data Replication Tools and Techniques for Managing Distributed Information. John Wiley & Sons.
    [25] Y. M. Huang and J. W. Ding, “Performance analysis of video storage server under initial delay bounds,” Journal of Systems Architecture, Vol. 46, No. 2,2000, pp. 163-179.
    [26] A. N. Mourad, “Issues in the design of a storage server for video on demand,” ACM Multimedia Systems, Vol. 4, No. 2, 1996, pp. 70-86.
    [27] Y. J. Lee and D. H. C. Du, “Adaptive load sharing and scheduling schemes for distributed continuous media delivery,” Proc. IEEE International Conference on Multimedia Computing and Systems, 1999, pp. 506-511.
    [28] David E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison Wesley Longman, Inc., 1989.
    [29] G. Rawlins, Foundations of Genetic Algorithms, Morgan Kaufmann, 1991.
    [30] Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1996.
    [31] Berson, A., Thearling, K., & Smith, S. (1999). Building Data Mining
    Applications for CRM. McGraw-Hill.
    [32] Abraham Silberschatz, Greg Gagne, Peter Baer Galvin, “Operating System Concepts 6/e, XP Edition”, JOHN WILEY, Inc., 2002.
    [33] Andrew S.Tanenbaum,“Modern Operating Systems”, 2nd edition, Prentice Hall press.
    [34] S. Chaudhuri and U. Dayal, “An Overview of Data Warehouse and OLAP Technology,” in ACM SIGMOD Record, 1997, pp: 65-74.
    [35] Q. Chen, U. Dayal and M. Hsu, “A Distributed OLAP Infrastructure for E-commerce,” Proc. IFCIS International Conference on Cooperative Information Systems, 1999, pp: 209-220.
    [36] J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan
    Kaufmaun, 2001.
    [37] Kimball et al,The Data Warehouse Lifecycle Toolkit,Wiley, 1998.
    [38] Richard J. Roiger and Michael W. Geatz, Data Mining: A Tutorial-Based Primer, Addison-Wesley, 2003.
    [39] N. Venkatasubramanian and S. Ramanathan, “Load management in distributed video servers,” Proc. 17th International Conference on Distributed Computing Systems, 1997, pp. 528-535.
    [40] Anker, T., Dolev, D., and Keidar, I., “Fault tolerant video-on-demand
    services,” Proceedings of the Nineteenth International Conference on
    Distributed Computing Systems, pp. 244-252, Austin, Texas, June 1999.
    [41] Golubchik, Leana, Richard R. Muntz, Cheng-Fu Chou, and Steven Berson,“Design of fault-tolerant large-scale VOD servers: with emphasis on
    high-performance and low-cost,” IEEE Transactions on Parallel and
    Distributed Systems, vol.12, no. 4, April 2001.
    [42] Shyu, I. J. and Shieh, S. P., “Balance workload and recovery load on
    distributed fault tolerant VOD system,” IEEE Journal on Communication
    Letters, vol.2, no. 10, October 1998.
    [43] J. F. Conde and Á. Viña, “Efficient memory management in video on demand servers,” Computer Communications, Vol. 23, No. 3, 2000, pp. 253-266.
    [44] K. L. Wu and P. S. Yu, “Consumption based buffer management for maximizing system throughput of a multimedia system,” Proc. Third IEEE International Conference on Multimedia Computing and Systems, 1996, pp. 164-171.

    無法下載圖示 全文公開日期 本全文未授權公開 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館:臺灣博碩士論文系統)
    QR CODE