Adaptive Sliding Piece Selection Window for BitTorrent Systems
• 2011
معلومات البحث
المؤلفون
Ahmed Z. Bayoumy, May A. Salama, Hala H. Zayed
الكلمات المفتاحية
Not Available
المجلة العلمية
Not Available
الناشر
Not Available
المجلد
Not Available
العدد
Not Available
الصفحات
Not Available
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
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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