ON UNBIASED SAMPLING FOR UNSTRUCTURED PEER-TO-PEER NETWORKS

Abstract

This paper presents a detailed examination of how the dynamic and heterogeneous nature of real-world peer-to-peer systems can introduce bias into the selection of representative samples of peer properties (e.g., degree, link bandwidth, number of files shared). We propose the Metropolises Random Walk with Backtracking (MRWB) as a viable and promising technique for collecting nearly unbiased samples and conduct an extensive simulation study to demonstrate that our technique works well for a wide variety of commonly-encountered peer-to-peer network conditions. We have implemented the MRWB algorithm for selecting peer addresses uniformly at random into a tool called ion – sampler. Using the Gnutella network, we empirically show that ion – sampler yields more accurate samples than tools that rely on commonly-used sampling techniques and results in dramatic improvements in efficiency and scalability compared to performing a full crawl.

Existing System:

In Previous studies of P2P systems typically relied on ad-hoc sampling techniques (e.g., [3], [4]) and provided valuable information concerning basic system behavior. However, lacking any critical assessment of the quality of these sampling techniques, the measurements resulting from these studies may be biased and consequently our understanding of P2P systems may be incorrect or misleading.

Proposed System:

The proposed MRWB algorithm assumes that the P2P System provides some mechanism to query a peer for a list of

Its neighbors—a capability provided by most widely deployed P2P systems. Our evaluations of the - tool shows

that the MRWB algorithm yields more accurate samples than previously considered sampling techniques. We quantify the observed differences, explore underlying causes, address the tool’s efficiency and scalability, and discuss the implications on accurate inference of P2P properties and high-fidelity modeling of P2P systems. While our focus is on P2P networks, many of our results apply to any large, dynamic, undirected graph where nodes may be queried for a list of their neighbors.

Requirements:

Hardware Requirement:-

                       Hard Disk                    -           20 GB

                       Monitor                       -           15’ Color with VGI card support

                       RAM                            -           Minimum 256 MB

                       Processor                    -           Pentium III and Above (or) Equivalent

                       Processor speed         -           Minimum 500 MHz

            Software Requirement:-

                       Operating System      -           Windows XP

                       Platform                      -           Visual Studio .Net 2005

                       Database                     -           SQL Server 2000

                       Languages                   -           C#.Net