Skip to main content
Log in

Perturbation analysis of theGI/GI/1 queue

  • Published:
Queueing Systems Aims and scope Submit manuscript

Abstract

We examine a family ofGI/GI/1 queueing processes generated by a parametric family of service time distributions,F(x,θ), and we show that under suitable conditions the corresponding customer stationary expectation of the system time is twice continuously differentiable with respect toθ. Expressions for the derivatives are given which are suitable for single run derivative estimation. These results are extended to parameters of the interarrival time distribution and expressions for the corresponding second derivatives (as well as partial second derivatives involving both interarrivai and service time parameters) are also obtained. Finally, we present perturbation analysis algorithms based on these expressions along with simulation results demonstrating their performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S. Asmussen,Applied Probability and Queues (Wiley, 1987).

  2. F. Baccelli and P. Brémaud,Palm Probabilities, Lecture Notes in Statistics (Springer, 1986).

  3. P. Brémaud and Lasgouttes, Stationary IPA estimates for non-smooth functions of theGI/GI/1/∞ workload, Rapp. de Rech. INRIA No. 1677 (1992).

  4. P. Konstantopoulos and M. Zazanis, Sensitivity analysis for stationary and ergodic queues, J. Appl. Prob. 24 (1992) 738–750.

    Google Scholar 

  5. P. Konstantopoulos and M. Zazanis, A note on the sensitivity analysis for stationary and ergodic queues, Adv. Appl. Prob. (1993), to appear.

  6. H. Chernoff, A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations, Ann. Math. Stat. 23 (1952) 493–507.

    Google Scholar 

  7. P. Glasserman,Gradient Estimation via Perturbation Analysis (Kluwer Academic, 1990).

  8. P. Glasserman, Structural conditions for perturbation analysis derivative estimation: Finite time performance indices, Oper. Res. 39 (1992) 724–738.

    Google Scholar 

  9. P. Glasserman, J.Q. Hu and S.G. Strickland, Strongly consistent steady state derivative estimates, Prob. Inf. Eng. Sci. 5 (1991) 391–414.

    Google Scholar 

  10. P.W. Glynn, Likelihood ratio gradient estimation: An overview,Proc. 1987 Winter Simulation Conf. (1987) pp. 366–375.

  11. A. Gut,Stopped Random Walks (Springer, 1988).

  12. P. Heidelberger, X.R. Cao, M.A. Zazanis and R. Suri, Convergence properties of infinitesimal perturbation analysis, Manag. Sci. 34 (1988) 1281–1302.

    Google Scholar 

  13. Y.C. Ho and X.R. Cao, Perturbation analysis and optimization of queueing networks, J. Optim. Theory Appl. 40 (1983) 559–582.

    Google Scholar 

  14. Y.C. Ho and X.R. Cao,Perturbation Analysis of Discrete Event Dynamic Systems (Kluwer Academic, 1991).

  15. J.Q. Hu, Convexity of sample path performance and strong consistency of infinitesimal perturbation analysis estimates, IEEE Trans. Auto. Contr. AC-37 (1992) 258–262.

    Google Scholar 

  16. J.F.C. Kingman, Some inequalities for the queueGI/G/1, Biometrika 49 (1962) 315–324.

    Google Scholar 

  17. L. Kleinrock,Queueing Systems I (Wiley, 1975).

  18. P. L'Ecuyer, P.N. Giroux and P.W. Glynn, Stochastic optimization by simulation: Some experiments with a simple steady state queue, Working Paper, Université Laval (1989).

  19. M.S. Meketon and P. Heidelberger, A renewal theoretic approach to bias reduction in regenerative simulations, Manag. Sci. 28 (1982) 173–181.

    Google Scholar 

  20. M.I. Reiman and A. Weiss, Sensitivity analysis of simulations via likelihood ratios, Oper. Res. 37 (1989) 830–844.

    Google Scholar 

  21. M.I. Reiman and B. Simon, Open queueing systems in light traffic, Math. Oper. Res. 14 (1989) 26–59.

    Google Scholar 

  22. A.C.M. van Rooij and W.H. Schikhof,A Second Course on Real Functions (Cambridge University Press, 1982).

  23. R.Y. Rubinstein, Sensitivity analysis and performance extrapolation for computer simulation models, Oper. Res. 37 (1989) 72–81.

    Google Scholar 

  24. A.N. Shiryayev,Probability (Springer, 1984).

  25. W.F. Stout,Almost Sure Convergence (Academic Press, 1974).

  26. R. Suri, Infinitesimal perturbation analysis for general discrete event systems, J. ACM 34 (1987) 686–717.

    Google Scholar 

  27. R. Suri, Perturbation analysis: The state of the art and research issues explained via theGI/G/1 queue, Proc. IEEE 77 (1989) 114–137.

    Google Scholar 

  28. R. Suri and Y.T. Leung, Single run optimization of discrete event systems: Experimental investigation for theM/M/1 queue, IIE Trans. 21 (1989) 35–49.

    Google Scholar 

  29. R. Suri and M.A. Zazanis, Perturbation analysis gives strongly consistent sensitivity estimates for theM/G/1 queue, Manag. Sci. 34 (1988) 39–64.

    Google Scholar 

  30. R.W. Wolff,Stochastic Modeling and the Theory of Queues (Prentice-Hall, 1989).

  31. M.A. Zazanis and R. Suri, Convergence rates of finite-difference sensitivity estimates for stochastic systems, Oper. Res. 41 (1993) 694–703.

    Google Scholar 

  32. M.A. Zazanis, Weak convergence of sample path derivatives forGI/G/1 queues,Proc. 25th Allerton Conf. on Communication, Control, and Computing (1989) pp. 297–304.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zazanis, M.A., Suri, R. Perturbation analysis of theGI/GI/1 queue. Queueing Syst 18, 199–248 (1994). https://doi.org/10.1007/BF01158763

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01158763

Keywords

Navigation