媒体内容分发系统的动态数据部署算法研究
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摘要
随着计算机技术的不断发展以及网络带宽的不断提高,流媒体服务被迅速推广。据最新的网络流量统计显示,流媒体服务已经成为当今互联网流量的主体。目前研究具有高承载能力,高可扩展性,高稳定性,低成本的可大规模商业化运营的流媒体内容分发系统成为全球流媒体应用领域的热点。与基于传统C/S架构的系统,基于CDN架构的系统,以及基于P2P架构的系统相比,近年来提出的基于融合CDN和P2P架构的媒体内容分发系统具有更好的优越性和发展潜力。本文基于这种最新提出的媒体内容分发系统,研究了其中的动态数据部署算法这一关键技术。
本文的研究工作主要包括下面几个方面:1.由于融合CDN和P2P技术的媒体内容分发系统比较复杂,本文把系统分成两层,即骨干层和边缘层,分别分析了每层要解决的主要问题。2.发现两层要解决的问题都是NP类问题,本文基于部分可观Markov决策过程(POMDP)以及基于观测的策略迭代优化算法和策略梯度优化算法分别给出了每层的动态数据部署算法。3.为了验证算法的性能,本文利用计算机仿真技术对一个具体媒体内容分发系统从简单情况到复杂情况逐步进行了仿真。4.对仿真结果进行分析和讨论,总结出一些规律和结论。
本文首次提出把POMDP的相关理论应用到媒体内容分发系统的动态数据部署算法的研究上,希望本文的研究工作能够拓展数据部署算法的研究思路,为该方面的进一步研究提供一些基础。
With the ever-increasing development of computer technology and improvement of network bandwidth, streaming media service is rapidly and widely used. According to the latest data of network flow, streaming media service has already been the main part of network flow nowadays. Currently, studies on content delivery network system of streaming media with high carrying ability, high expandability, high stability and low cost which can be commercially operated on a large scale has been a hotspot in the applied field of global streaming media. Compared with systems based on traditional C/S, CDN or P2P, the media content delivery system based on the combination of CDN and P2P put forward in recently years is of more advantages and development potential. The present paper, on the basis of the latest media content delivery system, examines one of the most important technologies: dynamic data deployment algorithm.
The present paper includes studies on the following aspects. Firstly, media content delivery system is very complex because of combining CDN and P2P, and the present paper divides the system into two layers (backbone layer and edge layer) and analyzes main problems to be solved in each layer respectively. Secondly, it is found that NP issues are the common problems to be solved in each layer and the present paper proposes dynamic data deployment algorithms for each layer respectively on the basis of POMDP, policy iteration algorithm and policy gradient algorithm. Thirdly, in order to verify the performance of the algorithm, the present paper employs computer simulation technology to conduct simulation on a concrete media content delivery system gradually from simple condition to more complicated ones. Fourthly, the present paper analyzes and discusses the simulation results and makes some conclusions.
The present paper applies relevant theories of POMDP to the study of dynamic data deployment algorithm of media content delivery system for the first time and endeavors to broaden the study thoughts of data deployment algorithm and lays a little foundation for the further study of the field.
本文的研究工作主要包括下面几个方面:1.由于融合CDN和P2P技术的媒体内容分发系统比较复杂,本文把系统分成两层,即骨干层和边缘层,分别分析了每层要解决的主要问题。2.发现两层要解决的问题都是NP类问题,本文基于部分可观Markov决策过程(POMDP)以及基于观测的策略迭代优化算法和策略梯度优化算法分别给出了每层的动态数据部署算法。3.为了验证算法的性能,本文利用计算机仿真技术对一个具体媒体内容分发系统从简单情况到复杂情况逐步进行了仿真。4.对仿真结果进行分析和讨论,总结出一些规律和结论。
本文首次提出把POMDP的相关理论应用到媒体内容分发系统的动态数据部署算法的研究上,希望本文的研究工作能够拓展数据部署算法的研究思路,为该方面的进一步研究提供一些基础。
With the ever-increasing development of computer technology and improvement of network bandwidth, streaming media service is rapidly and widely used. According to the latest data of network flow, streaming media service has already been the main part of network flow nowadays. Currently, studies on content delivery network system of streaming media with high carrying ability, high expandability, high stability and low cost which can be commercially operated on a large scale has been a hotspot in the applied field of global streaming media. Compared with systems based on traditional C/S, CDN or P2P, the media content delivery system based on the combination of CDN and P2P put forward in recently years is of more advantages and development potential. The present paper, on the basis of the latest media content delivery system, examines one of the most important technologies: dynamic data deployment algorithm.
The present paper includes studies on the following aspects. Firstly, media content delivery system is very complex because of combining CDN and P2P, and the present paper divides the system into two layers (backbone layer and edge layer) and analyzes main problems to be solved in each layer respectively. Secondly, it is found that NP issues are the common problems to be solved in each layer and the present paper proposes dynamic data deployment algorithms for each layer respectively on the basis of POMDP, policy iteration algorithm and policy gradient algorithm. Thirdly, in order to verify the performance of the algorithm, the present paper employs computer simulation technology to conduct simulation on a concrete media content delivery system gradually from simple condition to more complicated ones. Fourthly, the present paper analyzes and discusses the simulation results and makes some conclusions.
The present paper applies relevant theories of POMDP to the study of dynamic data deployment algorithm of media content delivery system for the first time and endeavors to broaden the study thoughts of data deployment algorithm and lays a little foundation for the further study of the field.
引文
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李衍杰. 2006.扩展Markov决策过程的性能灵敏度分析与优化[D]:[博士].合肥:中国科学技术大学.
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舒继武,薛巍,李必刚等. 2005.一种高可扩展存储网络系统TH-MSNS的研究与实现[J].计算机学报, 28(3): 326-333.
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汤子赢. 2001.操作系统原理[M].第2版.北京:清华大学出版社.
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杨戈,廖建新,朱晓民等. 2009.流媒体分发系统关键技术综述[J].电子学报, 37(1): 137-145.
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高宗敏. 2005.流媒体技术(1)[J].有线电视技术, (9): 48-55.
龚双瑾,刘多. 2003.下一代电信网的关键技术[M].北京:国防工业出版社.
侯成宝,宋建新. 2008. P2P与CDN结合实现IPTV业务[J].信息技术, 32(3): 102-105.
胡峰,孙国基,卫军胡. 2000.动态系统计算机仿真技术综述——计算机仿真建模[J].计算机仿真, 17(1): 1-7,11.
胡奇英,刘建庸. 2000.马尔科夫决策过程引论[M].西安:西安电子科技大学出版社.
廖建新,杨戈,朱晓民等. 2008.基于代理缓存的移动流媒体动态调度算法[J].计算机学报, 31(7): 1216-1223.
李衍杰. 2006.扩展Markov决策过程的性能灵敏度分析与优化[D]:[博士].合肥:中国科学技术大学.
李占波,袁清霞. 2008. CDN在流媒体系统中的应用[J].中国科技信息, (5): 102-103.
梁静国. 1993.管理系统仿真[M].哈尔滨:哈尔滨船舶工程学院出版社.
刘克. 2004.实用马尔科夫决策过程[M].北京:清华大学出版社.
刘亚杰. 2005. P2P流媒体内容分发关键技术研究[D]:[博士].长沙:国防科学技术大学.
马会. 2008.大规模流媒体传输技术的发展[J].计算机安全, (2): 21-25.
乔治. 2008. CDN与P2P结合的技术在流媒体分发和交付系统中的应用[J].邮电设计技术, (2): 44-48.
佘丹娴. 2006.内容分发网络(CDN)的发展与应用[J].中山大学研究生学刊:自然科学、医学版, 26(1): 95-103.
时明亮,赵玲玲. 2007.基于CDN与P2P技术的IPTV系统平台的设计与实现[J].北京城市学院学报, (02): 91-94.
舒继武,薛巍,李必刚等. 2005.一种高可扩展存储网络系统TH-MSNS的研究与实现[J].计算机学报, 28(3): 326-333.
孙江涛. 2003.流媒体将成为互联网应用主流[J].中国传媒科技,(07): 35-37.
汤子赢. 2001.操作系统原理[M].第2版.北京:清华大学出版社.
吴杰. 2008. P2P流媒体内容分发与服务关键技术研究[D]:[博士].上海:复旦大学.
杨波. 2006.流媒体系统的关键技术研究[D]:[博士].北京:北京邮电大学.
杨传栋,余镇危,王行刚. 2005.结合CDN与P2P技术的混合流媒体系统研究[J].计算机应用, 25(9): 2204-2207.
杨戈,廖建新,朱晓民等. 2009.流媒体分发系统关键技术综述[J].电子学报, 37(1): 137-145.
殷保群,奚宏生,周亚平. 2004.排队系统性能分析与Markov控制过程[M].合肥:中国科学技术大学出版社.
余俊,廖道训. 1984.最优化方法及其应用[M].武汉:华中工学院出版社.
余晓俊. 2008. CDN-P2P混合模式流媒体平台的研究和实现[D]:[硕士].上海:复旦大学.
钟玉琢,向哲,沈洪. 2003.流媒体和视频服务器[M].北京:清华大学出版社.
Abd-El-Malek M, CourtrightⅡW V, Cranor C, et al. 2005. Ursa Minor: Versatile Cluster-Based Storage[C]. In Proc. of the 4th USENIX Conference on File and Storage Technology(FAST’05), 59-72.
A. J. Cahill, C. J. Sreenan. 2003. Vcdn: A content distribution network for high quality video distribution[C]. In Proc. from Information Technology & Telecommunications, Letterkenny, Ireland.
A. R. Cassandra. 1998. A survey of POMDP applications[EB/OL]. [2009-3-6]. http://www.cassandra.org/pomdp/papers applications.pdf.
Arpaci-Dusseau A. C., Arpaci-Dusseau R. H., Bent J., et al. 2001. Manageable storage via adaptation in WiND[C]. In Proceedings of IEEE Int’l Symposium on Cluster Computing and the Grid (CCGrid'2001), 169~177.
Bolosky W, Draves J, Fitzgerald R, et al. 1996. The tiger video fileserver[C]. In Proceedings of the Sixth International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV 96).
Cao X. R. 2005. Basic Ideas for Event-Based Optimization of Markov Systems[J]. Discrete Event Dynamic Systems: Theory and Applications, 15(2): 169-197.
Cranor C. D., Green M., Kalmanek C., et al. 2001. Enhanced Streaming Services in a Content Distribution Network[J]. IEEE Internet Computing, 5(4): 66-75.
Dan A, Sitaram D. 1993. Buffer management policy for an on-demand video server[R]. IBM Research Report RC 19347.
Dongyan Xu, Sunil Suresh Kulkarni, Catherine Rosenberg, et al. 2006. A CDN-P2P Hybrid Architecture for Cost-Effective Streaming Media Distribution[J]. Multimedia Systems, 11(4): 383–399.
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