Distributed distance measurement for large-scale networks

There is an increasing trend in the Internet that a set of replicated providers are qualified for a service or resource request from a client. In this case, it is advantageous to select the best provider considering some distance measures, such as hop count or path latency. In this paper, we present a Group-based Distance Measurement Service (GDMS), which estimates and disseminates distance information of node-pairs in large-scale wide area networks. GDMS is fully distributed and does not rely on any centralized servers; thus is particularly suitable for the rapidly popularized peer-to-peer applications. The key concept in GDMS is Measurement Groups (MGroups). Nodes are self-organized into MGroups to form a hierarchical structure. A set of algorithms are proposed to handle network dynamics and optimize the group organization to reduce system costs as well as improve estimation accuracy. Moreover, a novel multicast-based algorithm is used for both intraand inter-group performance measurements. Performance evaluation over different network topologies shows that GDMS is scalable and provides effective distance information to upper-layer applications at a relatively low cost. Bo Li’s research is supported in part by grants from Research Grant Council (RGC) under contracts AoE/E-01/99 and HKUST6196/02E. J. Liu’s work is partially supported by a Microsoft fellowship. Part of this work was done when J. Liu and X. Zhang are interns at the Wireless and Networking Group, Microsoft Research, Asia.