Large-scale IP traffic matrix estimation based on simulated annealing

Traffic matrix (TM) is constructed by all origin-destination (OD) pairs (or flows) in a network, which describes the characteristics of IP networks from the global aspects. Though TM estimation is well-studied, TM is generally unavailable in a large-scale IP network and is difficult to be estimated accurately. This paper proposes a novel method of large-scale IP TM estimation, based on simulated annealing, termed the Simulated Annealing and Iterative Proportional Fitting Procedure (SAIPFP) method. Firstly, based on conventional simulated annealing, we propose a modified simulated annealing method, which considers sufficiently spatial-temporal correlations of OD flows and is combined with partial flow measurement. By the modified method, we can attain good estimations of TM. Secondly, combined with iterative proportional fitting procedure, the more accurate estimations are attained. Finally, we use the real data from the Abilene network to validate our method. The results show that our method can perform quickly the accurate estimation of large-scale IP TM, and track well its dynamics.