Optimizing capacity-heterogeneous unstructured P2P networks for random-walk traffic

Existing algorithms for utilizing high-capacity nodes in heterogeneous P2P systems (e.g.) often require unrealistically large node degree and high maintenance overhead in P2P networks with highly diverse node capacities and high churn. In this paper, we propose an unstructured P2P system that addresses these issues. We first prove that the overall throughput of search queries in a heterogeneous network is maximized if and only if traffic load through each node is proportional to its capacity. We then propose a system that achieves this traffic distribution by biasing search walks using the Metropolis-Hastings algorithm without requiring any special underlying topology. We finish the paper by comparing our method with Gia, where we find in simulation that the former outperforms the latter under all studied conditions, two novel saturation metrics introduced in this paper, and such end-to-end parameters as query success rate, latency, and query-hits.