Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-26T22:40:21.364Z Has data issue: false hasContentIssue false

Weak Convergence Limits for Sojourn Times in Cyclic Queues Under Heavy Traffic Conditions

Published online by Cambridge University Press:  14 July 2016

Hans Daduna*
Affiliation:
Hamburg University
Christian Malchin*
Affiliation:
Hamburg University
Ryszard Szekli*
Affiliation:
Wrocław University
*
Postal address: Department of Mathematics, Hamburg University, Bundesstrasse 55, 20146 Hamburg, Germany.
Postal address: Department of Mathematics, Hamburg University, Bundesstrasse 55, 20146 Hamburg, Germany.
∗∗∗Postal address: Mathematical Institute, Wrocław University, pl. Grunwaldzki 2/4, 50-384 Wrocław, Poland.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We consider sequences of closed cycles of exponential single-server nodes with a single bottleneck. We study the cycle time and the successive sojourn times of a customer when the population sizes go to infinity. Starting from old results on the mean cycle times under heavy traffic conditions, we prove a central limit theorem for the cycle time distribution. This result is then utilised to prove a weak convergence characteristic of the vector of a customer's successive sojourn times during a cycle for a sequence of networks with population sizes going to infinity. The limiting picture is a composition of a central limit theorem for the bottleneck node and an exponential limit for the unscaled sequences of sojourn times for the nonbottleneck nodes.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2008 

References

[1] Berger, A., Bregman, L. and Kogan, Y. (1999). Bottleneck analysis in multiclass closed queueing networks and its application. Queueing Systems 31, 217237.Google Scholar
[2] Billingsley, P. (1968). Convergence of Probability Measures. John Wiley, New York.Google Scholar
[3] Boxma, O. J. (1988). Sojourn times in cyclic queues – the influence of the slowest server. Comput. Performance Reliab. 1324.Google Scholar
[4] Boxma, O. J., Kelly, F. P. and Konheim, A. G. (1984). The product form for sojourn time distributions in cyclic exponential queues. J. Assoc. Comput. Mach. 31, 128133.Google Scholar
[5] Chen, H. and Yao, D. D. (2001). Fundamentals of Queueing Networks. Springer, New York.Google Scholar
[6] Chow, W. M. (1980). The cycle time distribution of exponential cyclic queues. J. Assoc. Comput. Mach. 27, 281286.Google Scholar
[7] Daduna, H. and Szekli, R. (2002). Conditional Job observer property for multitype closed queueing networks. J. Appl. Prob. 39, 865881.Google Scholar
[8] Gordon, W. J. and Newell, G. F. (1967). Closed queueing networks with exponential servers. Operat. Res. 15, 254265.Google Scholar
[9] Harrison, P. G. (1985). On normalizing constants in queueing networks. Operat. Res. 33, 464468.Google Scholar
[10] Kallenberg, O. (2002). Foundations of Modern Probability, 2nd edn. Springer, New York.Google Scholar
[11] Kelly, F. P. (1984). The dependence of sojourn times in closed queueing networks. In Mathematical Computer Performance and Reliability, eds Iazeolla, G. et, North-Holland, Amsterdam, pp. 111121.Google Scholar
[12] Kushner, H. J. (2001). Heavy Traffic Analysis of Controlled Queueing Networks and Communication Networks. Springer, New York.Google Scholar
[13] Lemoine, A. J. (1978). Networks of queues – a survey of weak convergence results. Manag. Sci. 24, 11751193.Google Scholar
[14] Malchin, C. and Daduna, H. (2005). An invariance property of sojourn times in cyclic networks. Operat. Res. Lett. 33, 18.Google Scholar
[15] Morrison, J. A. (1987). Conditioned response-time distribution for a large closed processor-sharing system in very heavy usage. SIAM J. Appl. Math. 47, 11171129.Google Scholar
[16] Reiman, M. I. (1982). The heavy traffic diffusion approximation for sojourn times in Jackson networks. In Applied Probability – Computer Sciences: The Interface, eds Disney, R. L. and Ott, T. J., Vol. II, Birkhäuser, Boston, MA, pp. 409421.Google Scholar
[17] Reiman, M. I. (1984). Some diffusion approximations with state space collapse. In Proc. Internat. Sem. Modeling Performance Eval. Methodol., eds Baccelli, F. and Fayolle, G., Springer, New York, pp. 20240.Google Scholar
[18] Schassberger, R. and Daduna, H. (1983). The time for a roundtrip in a cycle of exponential queues. J. Assoc. Comput. Mach. 30, 146150.Google Scholar
[19] Williams, R. J. (1996). On the approximation of queueing networks in heavy traffic. In Stochastic Networks – Theory and Applications, eds Kelly, F. P. et, Clarendon Press, Oxford, pp. 3556.Google Scholar