Network Topology Inference Based on Subset Structure Fusion

Abstract

Network topology measurement is an important component in network research. Network tomography is able to accurately infer network topology by using end-to-end measurement without cooperation of internal routers. Unfortunately, traditional network tomography methods can not accurately estimate topology in the non-stationary network due to the variability of traffic distribution. In this paper, we present a novel network topology inference method based on subset structure fusion for accurate topology inference in the non-stationary network. First, we propose an end-to-end measurement method named three-packet to accurately probe the three-leaf-nodes subset structures of the network without the assumption that the packet delay or loss follows a stable distribution. Second, we propose a metric for the shared path length based on the structural characteristics of the subset structures to fuse these subset structures into a correct complete topology. The analytical and simulation results show that our method is more applicable for topology inference in the non-stationary network compared with the existing methods.

Keywords

• End-to-end measurement
• network tomography
• non-stationary network
• subset structure fusion
• topology inference