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Reinforcement learning in BitTorrent systems

Title
Reinforcement learning in BitTorrent systems
Authors
Izhak-Ratzin R.Park H.Van Der Schaar M.
Ewha Authors
박형곤
SCOPUS Author ID
박형곤scopus
Issue Date
2011
Journal Title
Proceedings - IEEE INFOCOM
ISSN
0743-166XJCR Link
Citation
pp. 406 - 410
Indexed
SCOPUS scopus
Abstract
In this paper, we propose a BitTorrent-like protocol that replaces the peer selection mechanisms in the regular BitTorrent protocol with a novel reinforcement learning based mechanism. The inherent operation of P2P systems, which involves repeated interactions among peers over a long time period, allows peers to efficiently identify free-riders as well as desirable collaborators by learning the behavior of their associated peers. Thus, it can help peers improve their download rates and discourage free-riding (FR), while improving fairness. We model the peers' interactions in the BitTorrent-like network as a repeated interaction game, where we explicitly consider the strategic behavior of the peers. A peer that applies the reinforcement learning based mechanism uses a partial history of the observations on associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations with other peers, which would maximize the peer's long-term performance. © 2011 IEEE.
DOI
10.1109/INFCOM.2011.5935192
ISBN
9781424499212
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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