Hostname: page-component-7bb8b95d7b-lvwk9 Total loading time: 0 Render date: 2024-09-29T02:15:09.026Z Has data issue: false hasContentIssue false

Multiple channel queues in heavy traffic. III: random server selection

Published online by Cambridge University Press:  01 July 2016

Ward Whitt*
Affiliation:
Yale University

Extract

As in [4] and [5], we study service facilities with r arrival channels and s service channels. However, here we assume that customers, immediately upon arrival, randomly select one of the s service channels. Successive customers make this choice independently, choosing server i with probability pi, p1 + · · · + ps = 1. Customers are then served by the servers they select in order of their arrival without defections. The average processing rates as well as the server selection probabilities may vary from server to server, but again we assume the r arrival channels are independent and independent of the service channels. The service channels are not independent, however, because of the random server selection. For simplicity, we only consider a single queueing system; the extension to sequences follows immediately using the argument of [5].

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1970 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Billingsley, P. (1968) Convergence of Probability Measures. John Wiley and Sons, New York.Google Scholar
[2] Iglehart, D. (1968) Weak convergence of probability measures on product spaces with applications to sums of random vectors. Technical Report No. 109, Department of Operations Research and Department of Statistics, Stanford University.Google Scholar
[3] Iglehart, D. and Whitt, W. (1969) The equivalence of functional central limit theorems for counting processes and associated partial sums. Technical Report No. 5, Department of Operations Research, Stanford University.Google Scholar
[4] Iglehart, D. and Whitt, W. (1970a) Multiple channel queues in heavy traffic. I. Adv. Appl. Prob. 2, 150177.Google Scholar
[5] Iglehart, D. and Whitt, W. (1970b) Multiple channel queues in heavy traffic. II: sequences, networks, and batches. Adv. Appl. Prob. 2, 355369.Google Scholar
[6] Stone, C. (1963) Weak convergence of stochastic processes defined on semi-infinite time intervals. Proc. Amer. Math. Soc. 14, 694696.Google Scholar
[7] Whitt, W. (1968) Weak Convergence Theorems for Queues in Heavy Traffic. , Cornell University. (Technical Report No. 2, Department of Operations Research, Stanford University).Google Scholar
[8] Whitt, W. (1970a) Weak convergence of probability measures on the function space C[0, ∞). Ann. Math. Statist. 41 (To appear).Google Scholar
[9] Whitt, W. (1970b) Weak convergence of probability measures on the function space D[0, ∞). Forthcoming.Google Scholar