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Mike Chelen

Main Page - GenBioWiki - 0 views

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    GenBioWiki is the student home page for the Genetics, Bioinformatics, and Computational Biology (GBCB) program at Virginia Tech. Bioinformatics and computational biology provide a research platform to acquire, manage, analyze, and display large amounts of data, which in turn catalyze a systems approach to understanding biological organisms, as well as making useful predictions about their behavior in response to environmental and other perturbations. Moreover, bioinformatics is the study of biological systems and large biological data sets using analytical methods borrowed from computer science, mathematics, statistics, and the physical sciences. This transdisciplinary approach to research requires graduates with extensive cross-cultural professional and technical training and provides ample employment opportunities for Ph.D. graduates. [1]
Mike Chelen

USENIX IMC '05 Technical Paper - 0 views

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    Existing studies on BitTorrent systems are single-torrent based, while more than 85% of all peers participate in multiple torrents according to our trace analysis. In addition, these studies are not sufficiently insightful and accurate even for single-torrent models, due to some unrealistic assumptions. Our analysis of representative BitTorrent traffic provides several new findings regarding the limitations of BitTorrent systems: (1) Due to the exponentially decreasing peer arrival rate in reality, service availability in such systems becomes poor quickly, after which it is difficult for the file to be located and downloaded. (2) Client performance in the BitTorrent-like systems is unstable, and fluctuates widely with the peer population. (3) Existing systems could provide unfair services to peers, where peers with high downloading speed tend to download more and upload less. In this paper, we study these limitations on torrent evolution in realistic environments. Motivated by the analysis and modeling results, we further build a graph based multi-torrent model to study inter-torrent collaboration. Our model quantitatively provides strong motivation for inter-torrent collaboration instead of directly stimulating seeds to stay longer. We also discuss a system design to show the feasibility of multi-torrent collaboration.
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