A SCHEME FOR ADAPTIVE BIASING IN IMPORTANCE SAMPLING

This paper considers simulation of large networks. The quantities of interest, such as system failure, blocking or cell loss probability, are dependent on observations of rare events. For evaluation of such systems, previous work have 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 resources. A new algorithm for adaptive biasing of the simulation parameters is introduced. In addition, a flexible framework for modelling of both traffic and dependability aspects of the network is described. As a feasibility demonstration, the applicability of the proposed simulation framework is demonstrated by evaluation of time blocking probabilities 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 occurs on overloads and after link and node failures.

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