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.
Highlights
T HE rapid development of the network makes it increasingly difficult to manage the network
The method based on network tomography [5], [6] infers topology by using the path performance parameters obtained from end-to-end measurement
SIMULATION AND RESULTS we evaluate the performance of our method on the network simulator version 2 (NS2) [33] and compare its topology inference results with the methods based on the back-to-back packet
Summary
T HE rapid development of the network makes it increasingly difficult to manage the network. Existing network topology inference methods generally use well-designed end-to-end measurements such as backto-back packet [13] and "sandwich" packet [14] to obtain the path performance parameters of the network. In the non-stationary network, existing network topology inference methods can not obtain accurate metrics for shared path length to recover the correct topology. Based on the accurate measurement of subset structures via three-packet, this metric can precisely measure the shared path length of the topology in the non-stationary network and is conductive to estimate a correct binary topology. Our approach is more applicable to topology inference in the non-stationary network because we can accurately measure the shared path length of the subset structures via three-packet end-to-end measurement.
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