Journal of Computer Science

Analyzing the Peer to Peer Traffic Aggregation Using an Optimized Method

M. Sadish Sendil and N. Nagarajan

DOI : 10.3844/jcssp.2009.738.744

Journal of Computer Science

Volume 5, Issue 10

Pages 738-744


Problem statement: In the recent years, peer to peer networks have rapidly developed in the distributed and decentralized world of internet. Current research indicated that P2P applications were responsible for a substantial part of Internet traffic. Number of users embracing new P2P technology is also increasing fast. It is therefore important to understand the impact of the new P2P services on the existing Internet infrastructure and on legacy applications. The majority of unidentified traffic originates from Peer-to-Peer (P2P) applications like Napster, Gnuttella. Identification of P2P traffic seem to fail because their existence by using arbitrary ports. Approach: Proposed scheme concentrated on the factors and characteristics of P2P communications with payload issues on P2P application based on network traffic collection. The method used here was based on a set of heuristics derived from the robust properties of P2P traffic. Results: System demonstrated the method with current traffic data obtained from internet service providers. It had been found that flow sizes Vs holding times, behavior of P2P users Vs total active users were also analyzed and results of a heavy-tail analysis were described. Finally, system discussed the popularity distribution properties of P2P applications. Conclusion/Recommendations: This study suggested a very interesting and important result from a traffic dimensioning point of view: The ratio of active users and total users is almost constant. Results showed that unique properties of P2P application traffic seem to fade away during aggregation and characteristics of traffic will be similar to that of other non-P2P traffic aggregation.


© 2009 M. Sadish Sendil and N. Nagarajan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.