Combining partial direct measurements with an information-theoretic approach to estimate traffic matrix

Traffic matrices are essential for many traffic engineering tasks such as network management and capacity planning. However, it is difficult and cost to measure them accurately. The estimation of traffic matrix has usually been treated as a pure statistical inference problem. For example, infer the traffic matrix from the link load measurements and routing information. In practice, however, Internet Service Providers (ISPs) could measure partial traffic flows directly nowadays. In this paper, we propose a method to combine partial direct measurements of traffic matrix with one of the noticing method, the Minimum Mutual Information (MMI) method, to estimate the traffic matrix. Evaluation on real network data have demonstrated that with few levels of direct measurements of traffic flows could largely improve the performance of estimation, also the experiments have shown that choosing some large traffic flows to measure directly could have a great improvement on performance.