2026-09-19T01:26:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/664382022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Eytan H. Modiano.
Liman-Tinguiri, Karim
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-10-17T21:26:02Z
2011-10-17T21:26:02Z
2011
2011
http://hdl.handle.net/1721.1/66438
755631544
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).
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.
by Karim Liman-Tinguiri.
M.Eng.
82 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Using importance sampling to simulate queuing networks with heavy-tailed service time distributions
Thesis
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