Using importance sampling to simulate queuing networks with heavy-tailed service time distributions
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Eytan H. Modiano.
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Characterization of steady-state queue length distributions using direct simulation is generally computationally prohibitive. We develop a fast simulation method by using an importance sampling approach based on a change of measure of the service time in an M/G/1 queue. In particular, we present an algorithm for dynamically finding the optimal distribution within the parametrized class of delayed hazard rate twisted distributions of the service time. We run it on a M/G/1 queue with heavy-tailed service time distributions and show simulation gains of two orders of magnitude over direct simulation for a fixed confidence interval.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 81-82).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.