Structural insights on Internet traffic: Community overlapping and correlations

There is an increasing number of Internet applications, which leads to an increasing network capacity and availability. Internet traffic characterisation and application identification are, therefore, more important for efficient network management. In this paper, we construct flow graphs from detailed Internet traffic data collected from the public networks of Internet Service Providers. We analyse the community structures of the flow graph that is naturally formed by different applications. The community size, degree distribution of the community, and community overlap of 10 Internet applications are investigated. We further study the correlations between the communities from different applications. Our results provide deep insights into the behaviour Internet applications and traffic, which is helpful for both network management and user behaviour analysis.

[1]  Donald F. Towsley,et al.  A new virtual indexing method for measuring host connection degrees , 2011, 2011 Proceedings IEEE INFOCOM.

[2]  Shun-Zheng Yu,et al.  Internet Traffic Identification Using Community Detecting Algorithm , 2010, 2010 International Conference on Multimedia Information Networking and Security.

[3]  Xiaofei Wu,et al.  On the growth of Internet application flows: A complex network perspective , 2011, 2011 Proceedings IEEE INFOCOM.

[4]  M. E. J. Newman,et al.  Mixing patterns in networks: Empirical results and models , 2002 .

[5]  David A. Maltz,et al.  Network traffic characteristics of data centers in the wild , 2010, IMC '10.

[6]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[7]  Huaiyu Wan,et al.  Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks , 2012, Journal of Computer Science and Technology.

[8]  Michalis Faloutsos,et al.  BLINC: multilevel traffic classification in the dark , 2005, SIGCOMM '05.

[9]  Zhi-Li Zhang,et al.  YouTube traffic dynamics and its interplay with a tier-1 ISP: an ISP perspective , 2010, IMC '10.

[10]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[11]  Anirban Mahanti,et al.  Traffic classification using clustering algorithms , 2006, MineNet '06.

[12]  Yang,et al.  Modeling and Characterizing Internet Backbone Traffic , 2010 .

[13]  Kuai Xu,et al.  Internet Traffic Behavior Profiling for Network Security Monitoring , 2008, IEEE/ACM Transactions on Networking.

[14]  Lin Sen,et al.  Internet Traffic Classification Using C4.5 Decision Tree , 2009 .

[15]  Boleslaw K. Szymanski,et al.  Community detection using a neighborhood strength driven Label Propagation Algorithm , 2011, 2011 IEEE Network Science Workshop.

[16]  V. Carchiolo,et al.  Extending the definition of modularity to directed graphs with overlapping communities , 2008, 0801.1647.

[17]  Zhi-Li Zhang,et al.  Unveiling core network-wide communication patterns through application traffic activity graph decomposition , 2009, SIGMETRICS '09.

[18]  Judith Kelner,et al.  A Survey on Internet Traffic Identification , 2009, IEEE Communications Surveys & Tutorials.

[19]  Farnam Jahanian,et al.  Internet inter-domain traffic , 2010, SIGCOMM '10.

[20]  Maria Kihl,et al.  Traffic analysis and characterization of Internet user behavior , 2010, International Congress on Ultra Modern Telecommunications and Control Systems.