Accurate estimation of large-scale IP traffic matrix

Traffic matrix (TM) estimation which is an interesting and important research topic at present is used to conduct network management traffic detecting provisioning and so on However because of inherent characteristics in the IP network especially in the large-scale IP network TM estimation itself is highly under-constrained and so it is an very ill-posed problem how fast and accurately to attain large-scale IP TM estimation is a challenge Based on back-propagation neural network (BPNN) this paper proposes a novel method for large-scale IP TM estimation called BPNN TM estimation (BPTME) In contrast to previous methods BPTME can easily avoid the complex mathematical computation so that we can quickly estimate the TM The model of large-scale IP TM estimation built on top of BPNN whose outputs can sufficiently represent TM s spatial-temporal correlations ensures that we can attain an accurate estimation result Finally we use the real data from the Abilene Network to validate and evaluate BPTME Simulation results show that BPTME not only improves remarkably and holds better robustness but it can also make more accurate estimation of large-scale IP TM and track quickly its dynamics (C) 2010 Elsevier GmbH All rights reserved