Importance sampling for Jackson networks

Importance sampling for Jackson networks

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Article ID: iaor200971402
Country: Netherlands
Volume: 62
Issue: 1
Start Page Number: 113
End Page Number: 157
Publication Date: Jun 2009
Journal: Queueing Systems
Authors: ,
Keywords: Jackson network
Abstract:

Rare event simulation in the context of queueing networks has been an active area of research for more than two decades. A commonly used technique to increase the efficiency of Monte Carlo simulation is importance sampling. However, there are few rigorous results on the design of efficient or asymptotically optimal importance sampling schemes for queueing networks. Using a recently developed game/subsolution approach, we construct simple and efficient state-dependent importance sampling schemes for simulating buffer overflows in stable open Jackson networks. The sampling distributions do not depend on the particular event of interest, and hence overflow probabilities for different events can be estimated simultaneously. A by-product of the analysis is the identification of the minimizing trajectory for the calculus of variation problem that is associated with the sample-path large deviation rate function.

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