A Scheme for Adaptive Biasing in Importance Sampling

This paper considers rare event simulation of large networks. Typical quantities of interest are system failure, blocking or cell loss probability, which are dependent on observations of rare events. For evaluation of such systems, previous work has shown that simulation with a speed-up technique called importance sampling is an efficient means, provided that a good biasing of the simulation parameters exists. This paper addresses the unsolved problem of parameter biasing in large networks with well balanced resource utilisation. A new algorithm for adaptively biasing the simulation parameters is introduced. In addition, a flexible framework for modelling of both traffic and dependability aspects of a network is described. As a feasibility demonstration, the simulation framework is applied for evaluation of blocking in a network example. This network has traffic classes with different quality of service requirements, different capacity requirements, alternative routing strategies and preemptive priorities. Rerouting of traffic may occur in overload situations and after link and node failures.