Title
Estimating dynamic traffic matrices by using viable routing changes
Abstract
In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbone.
Year
DOI
Venue
2007
10.1109/TNET.2007.893227
IEEE/ACM Trans. Netw.
Keywords
Field
DocType
Routing,Telecommunication traffic,Traffic control,Spine,Fault diagnosis,Vectors,Linear systems,Error analysis,Tomography,Internet
Rank (linear algebra),Traffic variability,Linear system,Matrix (mathematics),Computer science,Word error rate,Computer network,Network tomography,Traffic engineering,Simple Network Management Protocol
Journal
Volume
Issue
ISSN
15
3
1063-6692
Citations 
PageRank 
References 
8
0.86
8
Authors
5
Name
Order
Citations
PageRank
Augustin Soule158435.76
Antonio Nucci269149.28
Rene Cruz31711256.63
E. Leonardi41830146.87
Nina Taft52109154.92