基于自适应随机行走的可扩展无偏抽样方法

A Scalable Unbiased Sampling Method Based on Multi-Peer Adaptive Random Walk

针对非结构化P2P系统中可扩展的快速无偏抽样问题,提出了一种基于多个peer自适应随机行走的抽样方法SMARW.在该方法中,基于代理随机行走选择一组临时的peer执行抽样过程,一次产生一组可调数目的抽样节点,提高了抽样速度,选择每次产生的抽样节点作为临时peer进行新的抽样过程,这种简单的方法可以保证系统具有近似最优的系统负载均衡程度.同时,SMARW利用自适应的分布式随机行走修正过程提高抽样过程的收敛速度.理论分析和模拟测试表明,SMARW方法具有较高的无偏抽样能力以及近似最优的系统负载均衡程度.

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-peer random walk process, a set of provisional peers are selected as agents which start the sampling processes, by which the sampling process is speeded up with receiving a set of tunable number samples each time; Meanwhile, after receiving new samples earlier agents are replaced with these new samples which repeat the sampling process. With this simple replacement, it can be guaranteed with high probability that the system can reach the optimal load balance; furthermore, SMARW adopts an adaptive distributed random walk adjustment process to increase the convergence rate of the sampling process. A detailed theorical analysis and performance evaluation confirm that SMARW has a high level of unbiased sampling and near-optimal load balancing capability.