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Tail behavior of the queue size and waiting time in a queue with discrete autoregressive arrivals

Published online by Cambridge University Press:  08 September 2016

Bara Kim*
Affiliation:
Korea University
Khosrow Sohraby*
Affiliation:
University of Missouri, Kansas City
*
Postal address: Department of Mathematics and Telecommunication Mathematics Research Center, Korea University, 1 Anam-dong, Sungbuk-ku, Seoul, 136-701, Korea. Email address: [email protected]
∗∗ Postal address: School of Computing and Engineering, University of Missouri, Kansas City, MO 64110, USA. Email address: [email protected]
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Abstract

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Autoregressive arrival models are described by a few parameters and provide a simple means to obtain analytical models for matching the first- and second-order statistics of measured data. We consider a discrete-time queueing system where the service time of a customer occupies one slot and the arrival process is governed by a discrete autoregressive process of order 1 (a DAR(1) process) which is characterized by an arbitrary stationary batch size distribution and a correlation coefficient. The tail behaviors of the queue length and the waiting time distributions are examined. In particular, it is shown that, unlike in the classical queueing models with Markovian arrival processes, the correlation in the DAR(1) model results in nongeometric tail behavior of the queue length (and the waiting time) if the stationary distribution of the DAR(1) process has infinite support. A complete characterization of the geometric tail behavior of the queue length (and the waiting time) is presented, showing the impact of the stationary distribution and the correlation coefficient when the stationary distribution of the DAR(1) process has finite support. It is also shown that the stationary distribution of the queue length (and the waiting time) has a tail of regular variation with index -β − 1, by finding an explicit expression for the tail asymptotics when the stationary distribution of the DAR(1) process has a tail of regular variation with index -β.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2006 

References

Addie, R. G. and Zukerman, M. (1994). Performance evaluation of a single server autoregressive queue. Austral. Telecommun. Res. 28, 2532.Google Scholar
Bingham, N. H., Goldie, C. M. and Teugels, J. L. (1987). Regular Variation. Cambridge University Press.CrossRefGoogle Scholar
Choe, J. and Shroff, N. B. (2000). Use of the supremum distribution of Gaussian processes in queueing analysis with long-range dependence and self-similarity. Stoch. Models 16, 209231.CrossRefGoogle Scholar
Choi, B. D., Kim, B., Hwang, G. U. and Kim, J.-K. (2004). The analysis of a multi server queue fed by discrete time autoregressive process of order 1. Operat. Res. Lett. 32, 8593.Google Scholar
Consul, P. C. and Jain, G. C. (1973). A generalisation of Poisson distribution. Technometrics 15, 791799.Google Scholar
Cox, D. R. (1955). Some statistical methods connected with series of events. J. R. Statist. Soc. B 17, 129164.Google Scholar
Cox, D. R. and Lewis, P. A. W. (1966). Statistical Analysis of Series of Events. Chapman and Hall, London.Google Scholar
Drezner, Z. and Farnum, N. (1994). A correlated Poisson distribution for correlated events. Commun. Statist. Theory Meth. 23, 841857.Google Scholar
Elwalid, A. et al. (1995). Fundamental bounds and approximations for ATM multiplexers with applications to video teleconferencing. IEEE J. Sel. Areas Commun. 13, 10041016.Google Scholar
Finch, P. D. (1963). The single server queueing system with non-recurrent input-process and Erlang service time. J. Austral. Math. Soc. 3, 220236.Google Scholar
Finch, P. D. and Pearce, C. (1965). A second look at a queueing system with moving average input process. J. Austral. Math. Soc. 5, 100106.CrossRefGoogle Scholar
Franken, P., König, D., Arndt, U. and Schmidt, V. (1982). Queues and Point Processes. Springer, Berlin.Google Scholar
Gaver, D. P. and Lewis, P. A. W. (1980). First-order autoregressive gamma sequences and point processes. Adv. Appl. Prob. 12, 727745.Google Scholar
He, J. and Sohraby, K. (2001). A new analysis framework for discrete time queueing systems with general stochastic sources. In Proc. INFOCOM 2001 (Anchorage, AK, April 2001), pp. 10751084.Google Scholar
Hwang, G. U. and Sohraby, K. (2003). On the exact analysis of a discrete-time queueing system with autoregressive inputs. Queueing Systems 43, 2941.Google Scholar
Hwang, G. U., Choi, B. D. and Kim, J.-K. (2002). The waiting time analysis of a discrete time queue with arrivals as an autoregressive process of order 1. J. Appl. Prob. 39, 619629.Google Scholar
Jacob, P. A. and Lewis, P. A. W. (1983). Time series generated by mixtures. J. Time Series Anal. 4, 1936.CrossRefGoogle Scholar
Kim, B., Chang, Y., Kim, Y. C. and Choi, B. D. (2006). A queueing system with discrete autoregressive arrivals. To appear in Performance Eval.Google Scholar
Lawrance, A. J. and Lewis, P. A. W. (1977). An exponential moving-average sequence and point process (EMA1). J. Appl. Prob. 14, 98113.Google Scholar
Srinivasan, S. K. (1974). Stochastic Point Processes and Their Applications. Griffin, London.Google Scholar
Szekli, R., Disney, R. L. and Hur, S. (1994). MR/GI/1 queues with positively correlated arrival stream. J. Appl. Prob. 31, 497514.CrossRefGoogle Scholar
Tin, P. (1985). A queueing system with Markov-dependent arrivals. J. Appl. Prob. 22, 668677.Google Scholar
Wolff, R. W. (1989). Stochastic Modeling and the Theory of Queues. Prentice Hall, Englewood Cliffs, NJ.Google Scholar