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Output analysis of a single-buffer multiclass queue: FCFS service

Published online by Cambridge University Press:  14 July 2016

V. G. Kulkarni*
Affiliation:
University of North Carolina
K. D. Glazebrook*
Affiliation:
University of Edinburgh
*
Postal address: Department of Operations Research, University of North Carolina, Chapel Hill, NC 27599-3180, USA. Email address: vkulkarn@email.unc.edu
∗∗ Postal address: School of Management, University of Edinburgh, Edinburgh EH8 9JY, UK.

Abstract

We consider an infinite capacity buffer where the incoming fluid traffic belongs to K different types modulated by K independent Markovian on-off processes. The kth input process is described by three parameters: (λk, μk, rk), where 1/λk is the mean off time, 1/μk is the mean on time, and rk is the constant peak rate during the on time. The buffer empties the fluid at rate c according to a first come first served (FCFS) discipline. The output process of type k fluid is neither Markovian, nor on-off. We approximate it by an on-off process by defining the process to be off if no fluid of type k is leaving the buffer, and on otherwise. We compute the mean on time τkon and mean off time τkoff. We approximate the peak output rate by a constant rko so as to conserve the fluid. We approximate the output process by the three parameters (λko, μko, rko), where λko = 1/τkoff, and μko = 1/τkon. In this paper we derive methods of computing τkon, τkoff and rko for k = 1, 2,…, K. They are based on the results for the computation of expected reward in a fluid queueing system during a first passage time. We illustrate the methodology by a numerical example. In the last section we conduct a similar output analysis for a standard M/G/1 queue with K types of customers arriving according to independent Poisson processes and requiring independent generally distributed service times, and following a FCFS service discipline. For this case we are able to get analytical results.

MSC classification

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2002 

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References

[1] Anick, D., Mitra, D., and Sondhi, M. M. (1982). Stochastic theory of a data-handling system with multiple sources. Bell System Tech. J. 61, 18711894.CrossRefGoogle Scholar
[2] Chandy, K. M., and Saur, C. H. (1978). Approximate methods for analyzing queueing network models of computing systems. ACM Comput. Surveys 10, 281317.CrossRefGoogle Scholar
[3] Gelenbe, E., and Mitrani, I. (1980). Analysis and Synthesis of Computer Systems. Academic Press, New York.Google Scholar
[4] Hirasawa, Y. (2000). Approximating traffic parameters in multi-class fluid networks. Doctoral Thesis, University of North Carolina.Google Scholar
[5] Kaspi, H., and Kella, O. (1996). Stability of feed-forward fluid networks with Lévy input. J. Appl. Prob. 33, 513532.CrossRefGoogle Scholar
[6] Kella, O. (1993). Parallel and tandem fluid networks with dependent Lévy inputs. Ann. Appl. Prob. 3, 682695.CrossRefGoogle Scholar
[7] Kella, O. (1996). Stability and non-product form of stochastic fluid networks with Lévy inputs. Ann. Appl. Prob. 6, 186199.CrossRefGoogle Scholar
[8] Kella, O., and Whitt, W. (1990). A tandem fluid network with Lévy input. Tech. Rep. 90-16, Yale University.Google Scholar
[9] Kella, O., and Whitt, W. (1990). Linear stochastic fluid networks. J. Appl. Prob. 36, 244260.CrossRefGoogle Scholar
[10] Kosten, L. (1974). Stochastic theory of a multi-entry buffer I. Delft Progress Rep. 1, 1018.Google Scholar
[11] Kuehn, P. J. (1979). Approximate analysis of general queueing networks by decomposition. IEEE Trans. Commun. 27, 113126.CrossRefGoogle Scholar
[12] Kulkarni, V. G. (1995). Modeling and Analysis of Stochastic Systems. Chapman and Hall, London.Google Scholar
[13] Kulkarni, V. G. (1997). Fluid models for single buffer systems. In Frontiers in Queueing: Models and Applications in Science and Engineering, ed. Dshalalow, J. H., CRC Press, Boca Raton, FL, pp. 321338.Google Scholar
[14] Kulkarni, V. G., and Rolski, T. (1994). Fluid model driven by an Ornstein–Uhlenbeck process. Prob. Eng. Inf. Sci. 8, 403417.CrossRefGoogle Scholar
[15] Kulkarni, V. G., and Tzenova, E. (2002). Mean first passage times in fluid queues. To appear in Operat. Res. Lett.CrossRefGoogle Scholar
[16] Reiser, M., and Kobayashi, H. (1974). Accuracy of the diffusion approximation for some queueing systems. IBM J. Res. Dev. 18, 110124.CrossRefGoogle Scholar
[17] Sevcik, K. C., Levy, A. I., Tripathi, S. K., and Zahorjan, J. L. (1977). Improving approximations of aggregated queueing network subsystems. In Computer Performance, eds Chandy, K. M. and Reiser, M., North-Holland, Amsterdam, pp. 122.Google Scholar
[18] Whitt, W. (1983). The queueing network analyzer. Bell System Tech. J. 62, 27792815.CrossRefGoogle Scholar