Guest editor's foreword

Abstract

In recent years, large scale network inference has attracted significant interest within the research community. On one front, considerable progress has been made on traffic matrix estimation. Solutions have been proposed to estimate the amount of traffic flowing between any pair of ingress and egress points within an IP network simply based on the total amount of traffic recorded over IP links. On another front, efforts are being made to detect the state of the network from end to end measurements using inference techniques or to infer the traffic workload by exploiting application behavior. In essence, the full instrumentation of the state of an IP network is still considered a cost prohibitive task and inference may be the only tool we have to understand the behavior of such large scale systems. The potential benefits of the proposed estimation techniques can be great. Accurate measurement of an IP traffic matrix is essential for network design and planning. Moreover, accurate estimation of the network state can facilitate troubleshooting and performance evaluation.