Neighbor selection for proportional fairness in P2P networks

This paper analyzes reciprocation strategies in peer-to-peer networks from the point of view of the resulting resource allocation. Our stated aim is to achieve through decentralized interactions a weighted proportionally fair allocation. We analyze the desirable properties of such allocation, as well as an ideal proportional reciprocity algorithm to achieve it, using tools of convex optimization. We then seek suitable approximations to the ideal allocation which impose practical constraints on the problem: numbers of open connections per peer, with transport layer-induced bandwidth sharing, and the need of random exploration of the peer-to-peer swarm. Our solution in terms of a Gibbs sampler dynamics characterized by a suitable energy function is implemented in simulation, comparing favorably with a number of alternatives.