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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Online Learning in BitTorrent Systems
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Online Learning in BitTorrent Systems

机译：BitTorrent系统中的在线学习

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摘要

We propose a BitTorrent-like protocol based on an online learning (reinforcement learning) mechanism, which can replace the peer selection mechanisms in the regular BitTorrent protocol. We model the peers' interactions in the BitTorrent-like network as a repeated stochastic game, where the strategic behaviors of the peers are explicitly considered. A peer that applies the reinforcement learning (RL)-based mechanism uses the observations on the 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 such that the peer can maximize its long-term performance. We have implemented the proposed mechanism and incorporated it into an existing BitTorrent client. Our experiments performed on a controlled Planetlab testbed confirm that the proposed protocol 1) promotes fairness and provides incentives to contributed resources, i.e., high capacity peers improve their download completion time by up to 33 percent, 2) improves the system stability and robustness, i.e., reduces the peer selection fluctuations by 57 percent, and (3) discourages free-riding, i.e., peers reduce their uploads to free-riders by 64 percent as compared to the regular BitTorrent protocol.
机译：我们提出了一种基于在线学习（强化学习）机制的类似BitTorrent的协议，该协议可以替代常规BitTorrent协议中的对等选择机制。我们在类似BitTorrent的网络中将对等方的交互建模为重复的随机游戏，其中明确考虑了对等方的战略行为。应用基于强化学习（RL）的机制的同级使用对相关同级的统计互惠行为的观察来确定其最佳响应，并估计对其预期效用的相应影响。该策略确定对等方的资源交换，以便对等方可以最大化其长期性能。我们已经实现了建议的机制，并将其合并到现有的BitTorrent客户端中。我们在受控Planetlab测试床上进行的实验证实，建议的协议1）促进了公平性并提供了对贡献资源的激励，即高容量对等方将其下载完成时间提高了33％，2）改善了系统稳定性和健壮性，即，将对等方选择的波动减少了57％，并且（3）阻止了搭便车，即与常规BitTorrent协议相比，对等方将其对搭便车的上载减少了64％。

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