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November 2004 Exponential penalty function control of loss networks
Garud Iyengar, Karl Sigman
Ann. Appl. Probab. 14(4): 1698-1740 (November 2004). DOI: 10.1214/105051604000000936

Abstract

We introduce penalty-function-based admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state. A major advantage of the penalty approach is that it avoids solving the associated dynamic program. However, a disadvantage of this approach is that it requires the capacity requested by individual requests to be sufficiently small compared to total available capacity. We first solve a related deterministic linear program (LP) and then translate an optimal solution of the LP into an admission control policy for the loss network via an exponential penalty function. We show that the penalty policy is a target-tracking policy—it performs well because the optimal solution of the LP is a good target. We demonstrate that the penalty approach can be extended to track arbitrarily defined target sets. Results from preliminary simulation studies are included.

Citation

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Garud Iyengar. Karl Sigman. "Exponential penalty function control of loss networks." Ann. Appl. Probab. 14 (4) 1698 - 1740, November 2004. https://doi.org/10.1214/105051604000000936

Information

Published: November 2004
First available in Project Euclid: 5 November 2004

zbMATH: 1065.93036
MathSciNet: MR2099649
Digital Object Identifier: 10.1214/105051604000000936

Subjects:
Primary: 90C59 , 93E03 , 93E35

Keywords: Exponential penalty , Loss networks , mathematical programming bounds , Stochastic control

Rights: Copyright © 2004 Institute of Mathematical Statistics

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Vol.14 • No. 4 • November 2004
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