Skip to main content
Log in

Palm calculus for a process with a stationary random measure and its applications to fluid queues

  • Published:
Queueing Systems Aims and scope Submit manuscript

Abstract

We consider a process associated with a stationary random measure, which may have infinitely many jumps in a finite interval. Such a process is a generalization of a process with a stationary embedded point process, and is applicable to fluid queues. Here, fluid queue means that customers are modeled as a continuous flow. Such models naturally arise in the study of high speed digital communication networks. We first derive the rate conservation law (RCL) for them, and then introduce a process indexed by the level of the accumulated input. This indexed process can be viewed as a continuous version of a customer characteristic of an ordinary queue, e.g., of the sojourn time. It is shown that the indexed process is stationary under a certain kind of Palm probability measure, called detailed Palm. By using this result, we consider the sojourn time processes in fluid queues. We derive the continuous version of Little's formula in our framework. We give a distributional relationship between the buffer content and the sojourn time in a fluid queue with a constant release rate.

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. D. Bertsimas and D. Nakazato, The distributional Little's law and its applications, preprint (1992), to appear in Oper. Res.

  2. P. Brémaud, An extension of Watanabe's theorem of characterization of Poisson processes over the positive real half line, J. Appl. Prob. 12 (1975) 396–399.

    Google Scholar 

  3. P. Brémaud,Point Processes and Queues: Martingale Dynamics (Springer, New York, 1981).

    Google Scholar 

  4. P. Brémaud, R. Kannurpatti and R. Mazumdar, Event and time averages: a review and some generalizations, Adv. Appl. Prob. 24 (1992) 377–411.

    Google Scholar 

  5. H. Chen and A. Mandelbaum, Discrete flow networks: Bottleneck analysis and fluid approximations, Math. Oper. Res. 16 (1991) 408–446.

    Google Scholar 

  6. D.J. Daley and D. Vere-Jones,An Introduction to the Theory of Point Processes (Springer, New York, 1988).

    Google Scholar 

  7. P. Franken, D. König, U. Arndt and V. Schmidt,Queues and Point Processes (Wiley, Chichester, 1982).

    Google Scholar 

  8. P.W. Glynn and W. Whitt, Extensions of the queueing relationsL=λW, Oper. Res. 37 (1982) 634–644.

    Google Scholar 

  9. O. Kallenberg,Random Measures (Academic Press, New York, 1983).

    Google Scholar 

  10. J. Keilson and L.D. Servi, A distributional form of Little's law, Oper. Res. Lett. 9 (1988) 239–247.

    Google Scholar 

  11. O. Kella, Parallel and tandem fluid networks with dependent Levy input, Ann. Appl. Prob. (1993), to appear.

  12. O. Kella and W. Whitt, A tandem fluid network with Levy input, in:Queueing and Related Models, eds. U.N. Bhat and I.V. Basawa (1992) pp. 112–127.

  13. I. Kino and M. Miyazawa, The stationary work in system of aG/G/1 gradual input queue, J. Appl. Prob. 30 (1993) 207–222.

    Google Scholar 

  14. R.M. Loynes, The stability of a queue with non-independent inter-arrival and service times, Proc. Camb. Phil. Soc. 58 (1962) 497–520.

    Google Scholar 

  15. J. Mecke, Stationäre zufällige Masse auf lokalkompakten Abelschen Gruppen, Z. Wahrscheinlich, verw. Geb. 9 (1967) 36–58.

    Google Scholar 

  16. B. Melamed and W. Whitt, On arrivals that see time averages, Oper. Res. 38 (1990) 156–172.

    Google Scholar 

  17. B. Melamed and W. Whitt, On arrivals that see time averages: A martingale approach, J. Appl. Prob. 27 (1990) 376–384.

    Google Scholar 

  18. M. Miyazawa, Time and customer processes in queues with stationary inputs, J. Appl. Prob. 14 (1977) 349–357.

    Google Scholar 

  19. M. Miyazawa, A formal approach to queueing processes in the steady state and their applications, J. Appl. Prob. 16 (1979) 332–346.

    Google Scholar 

  20. M. Miyazawa, Rate conservation law: a survey, Queueing Syst. 15 (1994) 1–58.

    Google Scholar 

  21. M. Miyazawa, Time-dependent rate conservation law for a process defined with a stationary marked point process and its applications, J. Appl. Prob. 31 (1994) 114–129.

    Google Scholar 

  22. M. Miyazawa and V. Schmidt, Risk processes with infinitely many small claims in a finite interval, in preparation (1993).

  23. M. Miyazawa and Y. Takahashi, Rate conservation principle for discrete-time queues, Queueing Syst. 12 (1992) 215–230.

    Google Scholar 

  24. H. Pan, H. Okazaki and I. Kino, Analysis of a gradual input model for bursty traffic in ATM,Proc. ITC 13, Copenhagen (1991) pp. 795–800.

    Google Scholar 

  25. N.U. Prabhu,Stochastic Storage Processes (Springer, New York, 1980).

    Google Scholar 

  26. P. Protter,Stochastic Integration and Differential Equations (Springer, New York, 1990).

    Google Scholar 

  27. T. Rolski and S. Stidham, Jr., Continuous versions of the queueing formulasL=λW andH=λG, Oper. Res. Lett. 2 (1983) 211–215.

    Google Scholar 

  28. C. Ryll-Nardzewski, Remarks on processes of calls,Proc. 4th Berkeley Symp. on Math. Statist. Prob., vol. 2 (1961) pp. 455–465.

    Google Scholar 

  29. K. Sigman and G. Yamazaki, Fluid models with burst arrivals: a sample-path analysis, Prob. Eng. Inf. Sci. (1992) 19–27.

  30. Y. Takahashi and M. Miyazawa, Relationship between queue-length and waiting time distributions in a priority queue with batch arrivals, Oper. Res. Soc. Japan (1993) 48–63.

  31. W. Whitt, A review ofL=λW and extensions, Queueing Syst. 9 (1991) 235–268.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Miyazawa, M. Palm calculus for a process with a stationary random measure and its applications to fluid queues. Queueing Syst 17, 183–211 (1994). https://doi.org/10.1007/BF01158694

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01158694

Keywords

Navigation