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Existing incentive schemes for peer-to-peer (P2P) file-sharing are rate-based, giving room for strategic peers to benefit from manipulative behaviors so as to treat honest peers unfairly. Specifically, strategic peers can achieve high performance by providing high upload rates which are useless to the system. On the other hand, honest peers suffer from getting low download rates even if they devote...
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...
BitTorrent is the dominating protocol in nearly all regions. Various reputation models and incentive mechanisms are proposed in recent years. However, many of them are designed for general peer-to-peer networks, only a few are designed for BitTorrent even though some models and mechanisms assert that they can be applied to BitTorrent-like systems. In this paper, we propose an incentive framework based...
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