Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T07:56:07.830Z Has data issue: false hasContentIssue false

Statistical efficiency of regenerative simulation methods for networks of queues

Published online by Cambridge University Press:  01 July 2016

Donald L. Iglehart*
Affiliation:
Stanford University
Gerald S. Shedler*
Affiliation:
IBM Research Laboratory, San Jose
*
Postal address: Department of Operations Research, Stanford University, Stanford, CA 94305, U.S.A.
∗∗Postal address: IBM Research Laboratory, San Jose, CA 95193, U.S.A.

Abstract

This paper is concerned with the assessment of the statistical efficiency of proposed regenerative simulation methods. We compare the efficiency of the ‘marked job' and ‘labelled jobs' methods for estimation of passage times in multiclass networks of queues with general service times. Using central limit theorem arguments, we show that the confidence intervals constructed for the expected value of a general function of the limiting passage time using the labelled jobs method are shorter than those obtained from the marked job method. This is consistent with intuition since the labelled jobs method extracts more passage-time information from a fixed-length simulation run.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1983 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Billingsley, P. (1968) Convergence of Probability Measures. Wiley, New York.Google Scholar
[2] Billingsley, P. (1979) Probability and Measure. Wiley, New York.Google Scholar
[3] Chung, K. L. (1974) A Course in Probability Theory, 2nd edn. Academic Press, New York.Google Scholar
[4] Crane, M. A. and Iglehart, D. L. (1975) Simulating stable stochastic systems: III, Regenerative processes and discrete event simulation. Operat. Res. 23, 3345.Google Scholar
[5] Hordijk, A., Iglehart, D. L. and Schassberger, R. (1976) Discrete-time methods for simulating continuous-time Markov chains. Adv. Appl. Prob. 8, 772778.Google Scholar
[6] Iglehart, D. L. (1971) Functional limit theorems for the queue GI/G/1 in light traffic. Adv. Appl. Prob. 3, 269281.CrossRefGoogle Scholar
[7] Iglehart, D. L. and Shedler, G. S. (1980) Regenerative Simulation of Response Times in Networks of Queues. Lecture Notes in Control and Information Sciences 26, Springer-Verlag, Berlin.Google Scholar
[8] Iglehart, D. L. and Shedler, G. S. (1981) Regenerative simulation of response times in networks of queues: statistical efficiency. Acta Informatica 15, 347363.Google Scholar
[9] Iglehart, D. L. and Shedler, G. S. (1981) Simulation for passage times in closed, multiclass networks of queues with general service times. IBM Research Report RJ3191. San Jose, California.Google Scholar
[10] Shedler, G. S. and Slutz, D. R. (1981) Irreducibility in closed multiclass networks of queues with priorities: passage times of a marked job. Performance Evaluation 1, 334343.Google Scholar
[11] Shedler, G. S. and Southard, J. (1981) Simulation for passage times in closed, multiclass networks of queues with unrestricted priorities. IBM Research Report RJ3190. San Jose, California.Google Scholar
[12] Shedler, G. S. and Southard, J. (1981) Regenerative simulation of networks of queues with general service times: passage through subnetworks. IBM J. Res. Development. To appear.CrossRefGoogle Scholar
[13] Whitt, W. (1980) Continuity of generalized semi-Markov processes. Math. Operat. Res. 5, 494501.Google Scholar