Non-uniform random membership management in peer-to-peer networks

Abstract— Existing random membership management algorithms provide each node with a small, uniformly random subset of global participants. However, many applications would benefit more from non-uniform random member subsets. For instance, non-uniform gossip algorithms can provide distancebased propagation bounds and thus information can reach nearby nodes sooner. In another example, Kleinberg shows that networks with random long-links following distance-based nonuniform distributions exhibit better routing performance than those with uniformly randomized topologies. In this paper, we propose a scalable non-uniform random membership management algorithm, which provides each node with a random membership subset with application-specified probability distributions—e.g., with probability inversely proportional to distances. Our algorithm is the first non-uniform random membership management algorithm with proved convergence and bounded convergence time. Moreover, our algorithm does ...

• Type: Conference
• Year: 2005
• Where: INFOCOM
• Authors: Ming Zhong, Kai Shen, Joel I. Seiferas
• Keywords: Communications, INFOCOM 2005, Non-uniform Random Membership, Random, Random Membership Management