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Inferring Invisible Traffic

Crovella, M (Boston)
Thursday 24 June 2010, 11:45-12.30

Seminar Room 1, Newton Institute


A traffic matrix encompassing the entire Internet would be very valuable. Unfortunately, from any given vantage point in the network, most traffic is invisible. In this talk I will describe results that hold some promise for this problem. First, I will show a new characterization result: traffic matrices (TMs) typically show very low effective rank. This result refers to TMs that are purely spatial (have no temporal component), over a wide range of spatial granularities. Next, I will define an inference problem whose solution allows one to infer invisible TM elements. This problem relies crucially on an atomicity property that I will define. Finally, I will show example solutions of this inference problem via two different methods: subset regression and matrix completion. The example consists of an AS inferring the amount of invisible traffic passing between other pairs of ASes. Using this example I will illustrate the accuracy of the methods as a function of spatial granularity.

This work is joint with Lokesh Setia, Vineet Bharti, Pankaj Setia, Gonca Gursun, and Anukool Lakhina.

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