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
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