Using importance sampling to simulate queuing networks with heavy-tailed service time distributions

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Liman-Tinguiri, Karim

Advisor dc:contributor.advisor

• Eytan H. Modiano.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/66438

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/66438