Adaptive Sliding Piece Selection Window for BitTorrent Systems
• 2011
Publication Information
Authors
Ahmed Z. Bayoumy, May A. Salama, Hala H. Zayed
Keywords
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Journal
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Publisher
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Volume
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Issue
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Pages
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publication.type
International
Paper Link
Open Link
Supplementary Materials
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Abstract
Peer to peer BitTorrent (P2P BT) systems are used for video-on-Demand (VoD) services.
Scalability problem could face this system and would cause media servers not to be able to
respond to the users’ requests on time. Current sliding window methods face problems like
waiting for the window pieces to be totally downloaded before sliding to the next pieces and
determining the window size that affects the video streaming performance. In this paper, a
modification is developed for BT systems to select video files based on sliding window method.
Developed system proposes using two sliding windows, High and Low, running simultaneously.
Each window collects video pieces based on the user available bandwidth, video bit rate and a
parameter that determines media player buffered seconds. System performance is measured and
evaluated against other piece selection sliding window methods. Results show that our method
outperforms the benchmarked sliding window methods.
Scalability problem could face this system and would cause media servers not to be able to
respond to the users’ requests on time. Current sliding window methods face problems like
waiting for the window pieces to be totally downloaded before sliding to the next pieces and
determining the window size that affects the video streaming performance. In this paper, a
modification is developed for BT systems to select video files based on sliding window method.
Developed system proposes using two sliding windows, High and Low, running simultaneously.
Each window collects video pieces based on the user available bandwidth, video bit rate and a
parameter that determines media player buffered seconds. System performance is measured and
evaluated against other piece selection sliding window methods. Results show that our method
outperforms the benchmarked sliding window methods.
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