An iterative tomogravity algorithm for the estimation of network traffic

TL;DR

This paper presents an iterative tomogravity algorithm for network traffic matrix estimation from a single snapshot, which is robust to missing data and does not need complete link load observations or parameter tuning.

Contribution

The proposed method offers a new approach that simplifies traffic estimation by reducing data requirements and enhancing robustness compared to existing techniques.

Findings

Controls estimation error effectively with full link data

Produces robust estimates with partial link data

Does not require tuning of penalty parameters

Abstract

This paper introduces an iterative tomogravity algorithm for the estimation of a network traffic matrix based on one snapshot observation of the link loads in the network. The proposed method does not require complete observation of the total load on individual edge links or proper tuning of a penalty parameter as existing methods do. Numerical results are presented to demonstrate that the iterative tomogravity method controls the estimation error well when the link data is fully observed and produces robust results with moderate amount of missing link data.