Measurement, modeling and enhancement of BitTorrent-based VoD system

  • Authors:

  • Zhen Ma;Ke Xu;Jiangchuan Liu;Haiyang Wang

  • Affiliations:

  • Department of Computer Science & Technology, Tsinghua University, Beijing 100084, China;Department of Computer Science & Technology, Tsinghua University, Beijing 100084, China;School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6;School of Computing Science, Simon Fraser University, British Columbia, Canada V5A 1S6

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2012

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Abstract

BitTorrent is one of the most popular Peer-to-Peer (P2P) applications for file sharing over the Internet. Video files take up a large proportion of space among the BitTorrent shared files. Recently, BitTorrent has attracted researchers' interests, as an alternative method of providing video on demand (VoD) service. In this paper, we concentrate on enabling BitTorrent to support VoD service in existing swarms while maintaining the download efficiency of file-sharing users. We first examine the content properties of the BitTorrent system to explore the demands and challenges of VoD service in BitTorrent swarms. The efficiency of BitTorrent for various piece selection policies is then compared through measurement on PlanetLab. We also use an optimization mathematical model to analyze the hybrid BitTorrent system in which downloading peers and streaming peers coexist. Both measurement results and model analysis indicate the problem of system efficiency decline in the BitTorrent-based VoD systems, in comparison with the original BitTorrent file-sharing system. Our proposed approach, unlike existing strategies that are limited to changing the piece selection policy to allow BitTorrent to support streaming services, modifies both piece and peer selection policies to provide a ''streaming while downloading'' service in the BitTorrent system with downloading peers. For the peer selection policy, a CAP (Closest-Ahead Peers) method is applied to make better use of the peers' upload bandwidths. For the piece selection policy, a sliding window-based hybrid method that combines the rarest-first policy with the sequential policy is proposed. To demonstrate the performance of our proposed approach, an evaluation is made using various metrics on PlanetLab. The results show that our proposed method has higher throughput and better streaming continuity than the sequential policy and BiToS.