Reinforcement Learning in BitTorrent Systems

Recent research efforts have shown that the popular BitTorrent protocol does not provide fair resource reciprocation and may allow free-riding. 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 (RL) based mechanism. Due to the inherent opration of P2P systems, which involves repeated interactions among peers over a long period of time, the peers can 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, while improving fairness in the system. 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, which applies the RL-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, thereby making foresighted decisions. We have implemented the proposed reinforcement-learning based mechanism and incorporated it into an existing BitTorrent client. We have performed extensive experiments on a controlled Planetlab test bed. Our results confirm that our proposed protocol (1) promotes fairness in terms of incentives to each peer's contribution e.g. high capacity peers improve their download completion time by up to 33\%, (2) improves the system stability and robustness e.g. reducing the peer selection luctuations by 57\%, and (3) discourages free-riding e.g. peers reduce by 64\% their upload to \FR, in comparison to the regular \BT~protocol.