CUFTI: Methods for core users finding and traffic identification in P2P systems

Peer-to-Peer system has achieved great success with millions of end users in the past several years. P2P traffic has occupied about 60–80 % of the total traffic volumes, which greatly consumes network bandwidth and causes congestions. To achieve the goal of efficacious P2P system management in the monitored network, in this paper we develop a framework named CUFTI (Core Users Finding and Traffic Identification). The core users are referred as long-lived peers, and we focus on life-time characteristics of coexisting peers within each snapshot of the overlay. Based on the analysis results of user’s behaviour in PPlive system, we develop an accurate model to forecast the peer’s residual life-time and identify the long-lived peers. Furthermore, we develop a flow identification model for P2P traffic management of those core users. Based on the analysis results of actual traffic traces, we find the P2P traffic flows are composed of data and control packets. Most of the control packets appear at the beginning and end of each flow to establish and close the communication between peers. We employ the direction and payload length of the control packets at the beginning of the flow as features to perform flow identification. Experimental results based on traces collected from the Northwest Region Center of CERNET (China Education and Research Network) show that the newly developed methods outperforms other existing methods with lower false negative rate (FNR) and false positive rate (FPR).

[1]  Jun Li,et al.  Optimal P2P Cache Sizing: A Monetary Cost Perspective on Capacity Design of Caches to Reduce P2P Traffic , 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.

[2]  Xiaoning Ding,et al.  Measurements, analysis, and modeling of BitTorrent-like systems , 2005, IMC '05.

[3]  Chih-Lin Hu,et al.  Distributed pairing for file sharing in large-scale peer-to-peer networks , 2011, 13th International Conference on Advanced Communication Technology (ICACT2011).

[4]  Oliver Spatscheck,et al.  Accurate, scalable in-network identification of p2p traffic using application signatures , 2004, WWW '04.

[5]  Indranil Gupta,et al.  Understanding overlay characteristics of a large-scale peer-to-peer IPTV system , 2010, TOMCCAP.

[6]  Chuan Wu,et al.  Distilling Superior Peers in Large-Scale P2P Streaming Systems , 2009, IEEE INFOCOM 2009.

[7]  Marco Mellia,et al.  Mining Unclassified Traffic Using Automatic Clustering Techniques , 2011, TMA.

[8]  Keith W. Ross,et al.  Inferring Network-Wide Quality in P2P Live Streaming Systems , 2007, IEEE Journal on Selected Areas in Communications.

[9]  Daniel Stutzbach,et al.  Understanding churn in peer-to-peer networks , 2006, IMC '06.

[10]  Jia Wang,et al.  Analyzing peer-to-peer traffic across large networks , 2004, IEEE/ACM Trans. Netw..

[11]  Zhi-Li Zhang,et al.  Inferring applications at the network layer using collective traffic statistics , 2010, International Teletraffic Congress.

[12]  Feng Wang,et al.  Stable Peers: Existence, Importance, and Application in Peer-to-Peer Live Video Streaming , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[13]  Hwangjun Song,et al.  A robust P2P video multicast streaming system under high peer-churn rate , 2011, 2011 IEEE 13th International Conference on Communication Technology.

[14]  Tao Qin,et al.  Dynamic Feature Analysis and Measurement for Large-Scale Network Traffic Monitoring , 2010, IEEE Transactions on Information Forensics and Security.

[15]  Bo Li,et al.  Opportunities and Challenges of Peer-to-Peer Internet Video Broadcast , 2008, Proceedings of the IEEE.

[16]  Panayiotis Mavrommatis,et al.  Identifying Known and Unknown Peer-to-Peer Traffic , 2006, Fifth IEEE International Symposium on Network Computing and Applications (NCA'06).

[17]  Chadi Barakat,et al.  Using host profiling to refine statistical application identification , 2012, 2012 Proceedings IEEE INFOCOM.

[18]  Michael Sirivianos,et al.  Free-riding in BitTorrent Networks with the Large View Exploit , 2007, IPTPS.

[19]  Renata Teixeira,et al.  Traffic classification on the fly , 2006, CCRV.

[20]  Antonio Pescapè,et al.  Early Classification of Network Traffic through Multi-classification , 2011, TMA.

[21]  Keith W. Ross,et al.  A Measurement Study of a Large-Scale P2P IPTV System , 2007, IEEE Transactions on Multimedia.

[22]  Chun-Jen Tsai,et al.  Visual sensitivity guided bit allocation for video coding , 2006, IEEE Transactions on Multimedia.

[23]  Kunwadee Sripanidkulchai,et al.  Considering Priority in Overlay Multicast Protocols Under Heterogeneous Environments , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[24]  Xinbo Jiang,et al.  Churn performance study of structured peer-to-peer overlay in supporting massively multiplayer online role playing games (MMORPGs) , 2011, 2011 17th IEEE International Conference on Networks.

[25]  Andrew W. Moore,et al.  Internet traffic classification using bayesian analysis techniques , 2005, SIGMETRICS '05.

[26]  Patrick Haffner,et al.  ACAS: automated construction of application signatures , 2005, MineNet '05.

[27]  Matthew Roughan,et al.  Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification , 2004, IMC '04.

[28]  Matthew Roughan,et al.  P2P the gorilla in the cable , 2003 .

[29]  Michalis Faloutsos,et al.  Is P2P dying or just hiding? [P2P traffic measurement] , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[30]  Michalis Faloutsos,et al.  Profiling-By-Association: a resilient traffic profiling solution for the internet backbone , 2010, CoNEXT.