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Queue-length balance equations in multiclass multiserver queues and their generalizations

Author

Listed:
  • Marko A. A. Boon

    (Eindhoven University of Technology)

  • Onno J. Boxma

    (Eindhoven University of Technology)

  • Offer Kella

    (The Hebrew University of Jerusalem)

  • Masakiyo Miyazawa

    (Tokyo University of Science)

Abstract

A classical result for the steady-state queue-length distribution of single-class queueing systems is the following: The distribution of the queue length just before an arrival epoch equals the distribution of the queue length just after a departure epoch. The constraint for this result to be valid is that arrivals, and also service completions, with probability one occur individually, i.e., not in batches. We show that it is easy to write down somewhat similar balance equations for multidimensional queue-length processes for a quite general network of multiclass multiserver queues. We formally derive those balance equations under a general framework. They are called distributional relationships and are obtained for any external arrival process and state-dependent routing as long as certain stationarity conditions are satisfied and external arrivals and service completions do not simultaneously occur. We demonstrate the use of these balance equations, in combination with PASTA, by (1) providing very simple derivations of some known results for polling systems and (2) obtaining new results for some queueing systems with priorities. We also extend the distributional relationships for a nonstationary framework.

Suggested Citation

  • Marko A. A. Boon & Onno J. Boxma & Offer Kella & Masakiyo Miyazawa, 2017. "Queue-length balance equations in multiclass multiserver queues and their generalizations," Queueing Systems: Theory and Applications, Springer, vol. 86(3), pages 277-299, August.
  • Handle: RePEc:spr:queues:v:86:y:2017:i:3:d:10.1007_s11134-017-9528-z
    DOI: 10.1007/s11134-017-9528-z
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    References listed on IDEAS

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    1. Martin Eisenberg, 1972. "Queues with Periodic Service and Changeover Time," Operations Research, INFORMS, vol. 20(2), pages 440-451, April.
    2. Yu-Sheng Zheng & Paul Zipkin, 1990. "A Queueing Model to Analyze the Value of Centralized Inventory Information," Operations Research, INFORMS, vol. 38(2), pages 296-307, April.
    3. Fakinos, D., 1991. "The relation between limiting queue size distributions at arrival and departure epochs in a bulk queue," Stochastic Processes and their Applications, Elsevier, vol. 37(2), pages 327-329, April.
    4. S. C. Borst & O. J. Boxma, 1997. "Polling Models With and Without Switchover Times," Operations Research, INFORMS, vol. 45(4), pages 536-543, August.
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    Cited by:

    1. Sem Borst & Onno Boxma, 2018. "Polling: past, present, and perspective," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 335-369, October.
    2. Kristoffel C. Pandiangan & Palti Maruli Tua Sitorus, 2017. "Analysis of Queue System to Improve the Quality of Service in GraPARI Telkomsel Banda Aceh," International Journal of Business and Economic Affairs (IJBEA), Sana N. Maswadeh, vol. 2(4), pages 220-226.

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