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Smoothing effect of the superposition of homogeneous sources in tandem networks

Published online by Cambridge University Press:  14 July 2016

Arie Hordijk*
Affiliation:
University of Leiden
Zhen Liu*
Affiliation:
INRIA
Don Towsley*
Affiliation:
University of Massachusetts
*
Postal address: Mathematical Institute, University of Leiden, The Netherlands
∗∗Postal address: INRIA, 2004 Route des Lucioles, B.P. 93, 06902 Sophia Antipolis, France. Email address: [email protected]
∗∗∗Postal address: Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA

Abstract

We analyze the smoothing effect of superposing homogeneous sources in a network. We consider a tandem queueing network representing the nodes that customers generated by these sources pass through. The servers in the tandem queues have different time varying service rates. In between the tandem queues there are propagation delays. We show that for arbitrary arrival and service processes which are mutually independent, the sum of unfinished works in the tandem queues is monotone in the number of homogeneous sources in the increasing convex order sense, provided the total intensity of the foreground traffic is constant. The results hold for both fluid and discrete traffic models.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2000 

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Footnotes

The work of this author was supported in part by NSF under awards ANI-980933 and CDA-9502639

References

Baccelli, F., and Bremaud, P. (1994). Elements of Queueing Theory. Springer, Berlin.Google Scholar
Baccelli, F., and Liu, Z. (1992). Comparison properties of stochastic decision free petri nets. IEEE Trans. Automat. Control 37, 19051920.CrossRefGoogle Scholar
Bäuerle, N. (1998). The advantage of small machines in a stochastic fluid production process. Math. Meth. Operat. Res. 47, 8397.Google Scholar
Bäuerle, N. (1999). How to improve the performance of ATM multiplexers. Operat. Res. Lett., 24, 8189.Google Scholar
Botvich, D., and Duffield, N. (1995). Large deviations, the shape of the loss curve, and economics of scale in large multiplexers. Queueing Systems 20, 293320.Google Scholar
Courcoubetis, C., and Weber, R. (1996). Buffer overflow asymptotics for a buffer handling many traffic sources. J. Appl. Prob. 33, 886903.CrossRefGoogle Scholar
Dumas, V., and Simonian, A. (2000). Asymptotic bounds for the fluid queue fed by subexponential On/Off sources. Adv. Appl. Prob. 33, 244255.CrossRefGoogle Scholar
Elwalid, A. D. Mitra, D., and Wentworth, R. (1995). A new approach for allocating buffers and bandwidth to heterogeneous regulated traffic in an ATM node. IEEE J. Sel. Areas Commun. 13, 11051127.Google Scholar
Koole, G., and Liu, Z. (1998). Stochastic bounds for queueing systems with multiple on-off sources. Prob. Eng. Inf. Sci., 12, 2548.Google Scholar
Koole, G., Liu, Z., and Towsley, D. (1999). Comparing queueing systems with heterogeneous on-off sources. Technical Rep. WS-530, Vrije Universiteit Amsterdam.Google Scholar
Mandjes, M. A note on large deviations for small buffers. Preprint.Google Scholar
Marshall, A. W., and Olkin, I. (1979). Inequalities: Theory of Majorization and Its Applications. Academic Press, New York.Google Scholar
Neuts, M., Matrix-Geometric Solutions in Stochastic Models, Johns Hopkins, Baltimore, Md. 1981.Google Scholar
Stoyan, D. (1983). Comparison Methods for Queues and Other Stochastic Models. English translation ed. Daley, D. J. John Wiley, New York.Google Scholar
Weiss, A. (1986). A new technique for analyzing large traffic systems. Adv. Appl. Prob. 18, 506532.Google Scholar