On unbiased sampling for unstructured peer-to-peer networks

This paper addresses the difficult problem of selecting representative samples of peer properties (e.g., degree, link bandwidth, number of files shared) in unstructured peer-to-peer systems. Due to the large size and dynamic nature of these systems, measuring the quantities of interest on every peer is often prohibitively expensive, while sampling provides a natural means for estimating system-wide behavior efficiently. However, commonly-used sampling techniques for measuring peer-to-peer systems tend to introduce considerable bias for two reasons. First, the dynamic nature of peers can bias results towards short-lived peers, much as naively sampling flows in a router can lead to bias towards short-lived flows. Second, the heterogeneous nature of the overlay topology can lead to bias towards high-degree peers. We present a detailed examination of the ways that the behavior of peer-to-peer systems can introduce bias and suggest the Metropolized Random Walk with Backtracking (MRWB) a...

• Type: Conference
• Year: 2006
• Where: IMC
• Authors: Daniel Stutzbach, Reza Rejaie, Nick G. Duffield, Subhabrata Sen, Walter Willinger
• Keywords: Bias, IMC 2006, Peer-to-peer Systems, Sampling Techniques