### Inferring Invisible Traffic

Crovella, M *(Boston)*

Thursday 24 June 2010, 11:45-12.30

Seminar Room 1, Newton Institute

#### Abstract

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.