Using importance sampling to simulate queuing networks with heavy-tailed service time distributions
Name
755631544-MIT.pdf
Description
Full printable version
Size
3.05 MB
Format
Adobe PDF
Checksum (MD5)
4121c8a7b3da8c6ea4e28d9e77e1b9c6
Author(s)
Liman-Tinguiri, Karim
Advisor(s)
Eytan H. Modiano.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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