Bayesian inference of network loss and delay characteristics with applications to TCP performance prediction

In large-scale dynamic communication networks, end systems cannot rely on the network itself to cooperate in characterizing its own behavior. This has prompted research activities on methods for inferring internal network behavior based on the external end-to-end network measurements. In particular, knowledge of the link losses and link delays inside the network is important for network management. However, it is impractical to directly measure packet losses or delays at every router. On the other hand, measuring end-to-end (from sources to destinations) losses or delays is relatively easy. We formulate the problems of link and delay estimation in a network based on end-to-end measurements as Bayesian inference problems and develop several Markov chain Monte Carlo (MCMC) algorithms to solve them. We show how these link loss and delay estimates can be used to predict point-to-point transfer control protocol (TCP) throughput in the network. We apply the proposed link loss and delay estimation algorithms, as well as the TCP throughput estimation algorithms, to data generated by the network simulator (ns-2) software and obtain good agreements between the theoretical results and the actual measurements.