To deal with the scalable and fast unbiased sampling problems in unstructured P2P systems, a sampling
method based on multi-peer adaptive random walk (SMARW) is proposed. In the method, based on the multi-peerrandom walk process, a set of provisional peers are selected as agents which start the sampling processes, by whichthe sampling process is speeded up with receiving a set of tunable number samples each time; Meanwhile, afterreceiving new samples earlier agents are replaced with these new samples which repeat the sampling process. Withthis simple replacement, it can be guaranteed with high probability that the system can reach the optimal loadbalance; furthermore, SMARW adopts an adaptive distributed random walk adjustment process to increase theconvergence rate of the sampling process. A detailed theorical analysis and performance evaluation confirm thatSMARW has a high level of unbiased sampling and near-optimal load balancing capability.
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