By Topic

A fast lightweight approach to origin-destination IP traffic estimation using partial measurements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Gang Liang ; Dept. of Stat., Univ. of California, USA ; N. Taft ; Bin Yu

In this paper, a novel approach is proposed for estimating traffic matrices. Our method, called PamTram for PArtial Measurement of TRAffic Matrices, couples lightweight origin-destination (OD) flow measurements along with a computationally lightweight algorithm for producing OD estimates. The first key aspect of our method is to actively select a small number of informative OD flows to measure in each estimation interval. To avoid the heavy computation of optimal selection, we use intuition from game theory to develop randomized selection rules, with the goals of reducing errors and adapting to traffic changes. We show that it is sufficient to measure only one flow per measurement period to drastically reduce errors-thus rendering our method lightweight in terms of measurement overhead. The second key aspect is an explanation and proof that an Iterative Proportional Fitting algorithm approximates traffic matrix estimates when the goal is a minimum mean-squared error; this makes our method lightweight in terms of computation overhead. A one-step error bound is provided for PamTram that bounds the average error for the worst scenario. We validate our method using data from Sprint's European Tier-1 IP backbone network and demonstrate its consistent improvement over previous methods.

Published in:

IEEE Transactions on Information Theory  (Volume:52 ,  Issue: 6 )