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Asymptotically achievable performance in ATM networks

Part of: Stochastic systems and control Special processes Operations research and management science Computer system organization

Published online by Cambridge University Press:  01 July 2016

Sem Borst  and
Debasis Mitra
Sem Borst*
Affiliation:
Bell Laboratories
Debasis Mitra*
Affiliation:
Bell Laboratories
*
∗ Postal address: Bell Laboratories, Lucent Technologies, P.O. Box 636, Murray Hill, NJ 07974-0636, USA.
∗ Postal address: Bell Laboratories, Lucent Technologies, P.O. Box 636, Murray Hill, NJ 07974-0636, USA.
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Abstract

The primary objective in the present paper is to gain fundamental understanding of the performance achievable in ATM networks as a function of the various system characteristics. We derive limit theorems that characterize the achievable performance in terms of the offered traffic, the admissible region, and the revenue measure. The insights obtained allow for substantial simplifications in the design of real-time connection admission control algorithms. In particular, we describe how the boundaries of admissible regions with convex complements may be linearized - thus reducing the admissible region - so as to obtain a convenient loss network representation. The asymptotic results for the achievable performance suggest that the potential reduction in revenue is immaterial in high-capacity networks. Numerical experiments confirm that the actual reduction is typically negligible, even in networks of moderate capacity.

Keywords

Achievable performanceATM networksconnection admission controlMarkov decision processesrevenue maximization

MSC classification

Primary: 60K30: Applications (congestion, allocation, storage, traffic, etc.)
Secondary: 60K20: Applications of Markov renewal processes (reliability, queueing networks, etc.) 68M20: Performance evaluation; queueing; scheduling 90B15: Network models, stochastic 90B22: Queues and service 93E20: Optimal stochastic control
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 30 , Issue 2 , June 1998 , pp. 568 - 585
Copyright
Copyright © Applied Probability Trust 1998 

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