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M. Perenyi, T. Dang, A. Gefferth, and S. Monlnar, "Identification and Analysis of Peer-to-Peer Traffic", in Journal of Communications 2006, Mar 2006.
Identification and Analysis of Peer-to-Peer Traffic
Authors: M. Perenyi
T. Dang
A. Gefferth
S. Monlnar
Published: Journal of Communications, 2006
URL:http://www.academypublisher.com/jcm/vol01/no07/jcm01073646.pdf
Entry Dates: 2009-02-11
Abstract: Recent measurement studies report that a significant portion of Internet traffic is unknown. It is very likely that the majority of the unidentified traffic originates from peer-to-peer (P2P) applications. However, traditional techniques to identify P2P traffic seem to fail since these applications usually disguise their existence by using arbitrary ports. In addition to the identification of actual P2P traffic, the characteristics of that type of traffic are also scarcely known. The main purpose of this paper is twofold. First, we propose a novel identification method to reveal P2P traffic from traffic aggregation. Our method does not rely on packet payload so we avoid the difficulties arising from legal, privacy-related, financial and technical obstacles. Instead, our method is based on a set of heuristics derived from the robust properties of P2P traffic. We demonstrate our method with current traffic data obtained from one of the largest Internet providers in Hungary. We also show the high accuracy of the proposed algorithm by means of a validation study. Second, several results of a comprehensive traffic analysis study are reported in the paper. We show the daily behavior of P2P users compared to the non-P2P users. We present our important finding about the almost constant ratio of the P2P and total number of users. Flow sizes and holding times are also analyzed and results of a heavy-tail analysis are described. Finally, we discuss the popularity distribution properties of P2P applications. Our results show that the unique properties of P2P application traffic seem to fade away during aggregation and characteristics of the traffic will be similar to that of other non-P2P traffic aggregation.
Results:
  • datasets: collected at one of the largest Internet providers in Hungary in May 2005; 1) Callrecords 1 inbound: 22nd July 2005, 457.84GB; 2) Callrecords 1 outbound: 22nd July 2005, 93.95GB; 3) Callrecords 2 outbound: 4th April 2006, 175.62GB;
  • propose a novel identification method to reveal P2P traffic, a set of heuristics;
  • give a comprehensive traffic analysis study, show that the unique properities of P2P application traffic seem to fade away during aggregation and characteristics of the traffic will be similar to that of other non-P2P traffic aggregation;
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