Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T10:26:57.207Z Has data issue: false hasContentIssue false

Relating Time and Customer Averages for Queues Using ‘forward’ Coupling from the Past

Published online by Cambridge University Press:  14 July 2016

Erol A. Peköz*
Affiliation:
Boston University
Sheldon M. Ross*
Affiliation:
University of Southern California
*
Postal address: Department of Operations and Technology Management, Boston University, 595 Commonwealth Avenue, Boston, MA 02215, USA. Email address: [email protected]
∗∗Postal address: Department of Industrial and System Engineering, University of Southern California, Los Angeles, CA 90089, USA.
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 give a new method for simulating the time average steady-state distribution of a continuous-time queueing system, by extending a ‘read-once’ or ‘forward’ version of the coupling from the past (CFTP) algorithm developed for discrete-time Markov chains. We then use this to give a new proof of the ‘Poisson arrivals see time averages’ (PASTA) property, and a new proof for why renewal arrivals see either stochastically smaller or larger congestion than the time average if interarrival times are respectively new better than used in expectation (NBUE) or new worse than used in expectation (NWUE).

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2008 

References

[1] Brèmaud, P., Kannurpatti, R. and Mazumdar, R. (1992). Event and time averages: a review. Adv. Appl. Prob. 24, 377411.Google Scholar
[2] Corcoran, J. N. and Tweedie, R. L. (2001). Perfect sampling of ergodic Harris chains. Ann. Appl. Prob. 11, 438451.CrossRefGoogle Scholar
[3] Foss, S. G., Tweedie, R. L. and Corcoran, J. N. (1998). Simulating the invariant measures of Markov chains using backward coupling at regeneration times. Prob. Eng. Inf. Sci. 12, 303320.Google Scholar
[4] Heyman, D. P. and Stidham, S. Jr. (1980). The relation between customer and time averages in queues. Operat. Res. 28, 983994.CrossRefGoogle Scholar
[5] Köning, D. and Schmidt, V. (1980). Stochastic inequalities between customer-stationary and time-stationary characteristic of queueing systems with point processes. J. Appl. Prob. 17, 768777.Google Scholar
[6] Köning, D. and Schmidt, V. (1981). Relationships between time- and customer-stationary characteristics of service systems. In Point Processes and Queueing Problems, eds Bartfai, P. and Tomko, J., North-Holland, Amsterdam, pp. 181225.Google Scholar
[7] Melamed, B. and Whitt, W. (1990). On arrivals that see time averages. Operat. Res. 38, 156172.Google Scholar
[8] Melamed, B. and Whitt, W. (1990). On arrivals that see time averages: a martingale approach. J. Appl. Prob. 27, 376384.Google Scholar
[9] Melamed, B. and Yao, D. D. (1995). The ASTA property. In Advances in Queueing, ed. Dshalalow, J. H., CRC, Boca Raton, FL, pp. 195224.Google Scholar
[10] Niu, S. (1984). Inequalities between arrival averages and time averages in stochastic processes arising from queueing theory. Operat. Res. 32, 785795.Google Scholar
[11] Propp, J. and Wilson, D. (1996). Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures Algorithms 9, 223252.3.0.CO;2-O>CrossRefGoogle Scholar
[12] Ross, S. and Peköz, E. A. (2007). A Second Course in Probability. ProbabilityBookstore.com, Boston, MA.Google Scholar
[13] Shanthikumar, G. and Zazanis, M. (1999). Inequalities between event and time averages. Prob. Eng. Inf. Sci. 13, 293308.Google Scholar
[14] Wilson, D. (2000). How to couple from the past using a read-once source of randomness. Random Structures Algorithms 16, 85113.Google Scholar
[15] Wolff, R. (1982). Poisson arrivals see time averages. Operat. Res. 30, 223231.Google Scholar