Optimal Resource Capacity Management for Stochastic Networks

A. B. Dieker (), S. Ghosh () and M. S. Squillante ()
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A. B. Dieker: Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
S. Ghosh: Mathematical Sciences Department, IBM Research, Yorktown Heights, New York 10598
M. S. Squillante: Mathematical Sciences Department, IBM Research, Yorktown Heights, New York 10598

Operations Research, 2017, vol. 65, issue 1, 221-241

Abstract: We develop a framework for determining the optimal resource capacity of each station composing a stochastic network, motivated by applications arising in computer capacity planning and business process management. The problem is mathematically intractable in general and therefore one typically resorts to either simplistic analytical approximations or time-consuming simulation-based optimization methods. Our solution framework includes an iterative methodology that relies only on the capability of observing the queue lengths at all network stations for a given resource capacity allocation. We theoretically investigate this proposed methodology for single-class Brownian tree networks and illustrate the use of our framework and the quality of its results through computational experiments.

Keywords: capacity allocation; capacity planning; queueing networks; resource capacity management; stochastic networks; stochastic approximation; simulation optimization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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