计算机应用 ›› 2013, Vol. 33 ›› Issue (04): 931-934.DOI: 10.3724/SP.J.1087.2013.00931

• 网络与通信 • 上一篇    下一篇

基于改进粒子群算法的P2P流媒体数据调度策略

陈光喜1,李振兴1,刘卓军2   

  1. 1. 桂林电子科技大学 广西可信软件重点实验室,广西 桂林541004
    2. 中国科学院 数学与系统科学研究院,北京 100190
  • 收稿日期:2012-10-16 修回日期:2012-11-30 出版日期:2013-04-01 发布日期:2013-04-23
  • 通讯作者: 陈光喜
  • 作者简介:陈光喜(1971-),男,四川金堂人,教授,博士,主要研究方向:智能软件与算法、信息安全、符号计算;李振兴(1988-),男,湖南常德人,硕士,主要研究方向:智能算法、网络;刘卓军(1958-),男,山东即墨人,教授,博士,主要研究方向:符号计算、计算机数学。
  • 基金资助:

    广西可信软件重点实验室开放基金资助项目(kx201213);中国科学院数学机械化重点实验室开放基金资助项目

Data scheduling strategy in P2P streaming system based on improved particle swarm optimization algorithm

1,LI Zhenxing1,LIU Zhuojun2   

  1. 1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
    2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2012-10-16 Revised:2012-11-30 Online:2013-04-01 Published:2013-04-23
  • Supported by:

    GUANGXIKEYLAB OF TRUSTED SOFTWARE

摘要: 根据P2P流媒体数据调度特点,改进了粒子群优化算法,并提出适用于离散粒子群算法(MDPSOA)的数字串编码方式。调度策略引入资源紧急度以及资源稀缺度来选择调度数据片,然后用改进离散粒子群算法来进行节点寻优,找出最优调度节点集。最后通过实验仿真算法收敛性、数据调度时间、网络利用带宽和节点负载平衡,从而验证调度策略的可行性和有效性。

关键词: 对等网络, 流媒体, 数据调度, 粒子群算法, 编码

Abstract: Data scheduling strategy in Peer-to-Peer (P2P) media streaming is the key research of the P2P media streaming system. A Particle Swarm Optimization (PSO) algorithm was modified according to P2P streaming data scheduling features and the style of digital encoding string for the algorithm was proposed in this paper. The data scheduling strategy to choose the data chunk took account of resource urgency and scarcity degree. The modified discrete particle swarm algorithm was used to choose the peers to get the optimal scheduling peers set. In order to verify the feasibility and effectiveness of the algorithm, experiments were done to simulate the convergence of the algorithm, the scheduling time, the P2P network uplink bandwidth utilization and the load balancing of peers.

Key words: Peer-to-Peer (P2P), media streaming, data scheduling, Particle Swarm Optimization (PSO) algorithm, encoding

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