Probability-Based Routing Symmetry Metrics

In communication networks, if streams between two endpoints follow the same physical paths for both forward and reverse direction, they are symmetric. Routing asymmetry affects several protocols, and impacts part of traffic analysis techniques. We propose two routing symmetry metrics to express different meanings when talking about routing symmetry, namely, (1) the forward and reverse flows coming from one node to another are exactly the same, and (2) one single node is visited by both flows. The two metrics are termed as identity symmetry and cross symmetry, respectively. Then, we build a model to link the macroscopic symmetry with the microscopic routing behavior, and present some analysis results, thus make it possible to design a routing algorithm with some desired symmetry. The simulation and dataset study show that routing algorithms that generate next hop randomly will lead to a symmetric network, but it is not the case for Internet. Because the paths of Internet are heavily dominated by a small number of prevalent routes, Internet is highly asymmetry.

[1]  Vern Paxson,et al.  End-to-end routing behavior in the Internet , 1996, TNET.

[2]  Yuval Shavitt,et al.  On the Diversity, Stability and Symmetry of End-to-End Internet Routes , 2010, 2010 INFOCOM IEEE Conference on Computer Communications Workshops.

[3]  Maurizio Dusi,et al.  Estimating routing symmetry on single links by passive flow measurements , 2010, IWCMC.

[4]  Walter Willinger,et al.  The many facets of internet topology and traffic , 2006, Networks Heterog. Media.

[5]  Anthony McGregor,et al.  Flow Clustering Using Machine Learning Techniques , 2004, PAM.

[6]  Grenville J. Armitage,et al.  A survey of techniques for internet traffic classification using machine learning , 2008, IEEE Communications Surveys & Tutorials.

[7]  Yuval Shavitt,et al.  Stability and Symmetry of Internet Routing , 2009, IEEE INFOCOM Workshops 2009.

[8]  Sergio D. Servetto,et al.  Constrained random walks on random graphs: routing algorithms for large scale wireless sensor networks , 2002, WSNA '02.

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  M. Engin Tozal.  Autonomous system ranking by topological characteristics: A comparative study , 2017, 2017 Annual IEEE International Systems Conference (SysCon).

[12]  Zahir Tari,et al.  A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis , 2014, IEEE Transactions on Emerging Topics in Computing.

[13]  Marcelo R. Hilario,et al.  Random walk on random walks: Low densities , 2020, The Annals of Applied Probability.

[14]  Ying Zhang,et al.  Understanding network delay changes caused by routing events , 2007, SIGMETRICS '07.

[15]  Luca Salgarelli,et al.  Impact of Asymmetric Routing on Statistical Traffic Classification , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[16]  Jie Wu,et al.  Robust Network Traffic Classification , 2015, IEEE/ACM Transactions on Networking.

[17]  Dong Fang,et al.  Identity Routing Symmetry Metrics for Routing Behavior , 2016 .

[18]  Elena Baralis,et al.  SeLeCT: Self-Learning Classifier for Internet Traffic , 2014, IEEE Transactions on Network and Service Management.

[19]  Yin Zhang,et al.  On AS-level path inference , 2005, SIGMETRICS '05.

[20]  Michalis Faloutsos,et al.  Quantifying routing asymmetry in the Internet at the AS level , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..