Hybrid Techniques for Large-Scale IP Traffic Matrix Estimation

The information on the volume of traffic flowing between all possible origin and destination pairs in an Internet Protocol (IP) network during a given period of time is generally referred to as traffic matrix (TM). This information, which is very important for various traffic engineering tasks, is very costly and difficult to obtain on large operational IP network, consequently, it is often inferred from readily available link load measurements. Several techniques have been proposed for estimation of traffic matrix on operational IP network from measured link load data and routing information. However, because the problem is a linear ill-posed and has no unique or direct solution, mathematically speaking, many of these techniques rely on some assumptions about the distribution of origin-destination (OD) flows. The validity of these assumptions and resulting prior estimates affect the performance and accuracy of the techniques. In this paper, we demonstrated the result of two hybrid techniques formed by combining iterative proportional fitting (IPF) and fanout estimation with well-known techniques such as tomogravity (TG), entropy maximization (EM) and Neural Network (NN) in producing improved estimation of the traffic matrix from link load data and sampled flow measurement. The low overhead of these hybrid techniques, as well as the significant reduction in error achieved, compared to using the gravity or similar prior estimates, makes them worthwhile approaches that can be adopted by Internet service providers (ISPs) for large-scale IP traffic matrix estimation.