Optimal buffer size for a stochastic processing network in heavy traffic |
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Authors: | Arka P Ghosh Ananda P Weerasinghe |
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Institution: | (1) Department of Statistics, Iowa State University, 303 Snedecor Hall, Ames, IA 50011-1210, USA;(2) Department of Mathematics, Iowa State University, 396 Carver Hall, Ames, IA 50011, USA |
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Abstract: | We consider a one-dimensional stochastic control problem that arises from queueing network applications. The state process
corresponding to the queue-length process is given by a stochastic differential equation which reflects at the origin. The
controller can choose the drift coefficient which represents the service rate and the buffer size b>0. When the queue length reaches b, the new customers are rejected and this incurs a penalty. There are three types of costs involved: A “control cost” related
to the dynamically controlled service rate, a “congestion cost” which depends on the queue length and a “rejection penalty”
for the rejection of the customers. We consider the problem of minimizing long-term average cost, which is also known as the
ergodic cost criterion. We obtain an optimal drift rate (i.e. an optimal service rate) as well as the optimal buffer size
b
*>0. When the buffer size b>0 is fixed and where there is no congestion cost, this problem is similar to the work in Ata, Harrison and Shepp (Ann. Appl.
Probab. 15, 1145–1160, 2005). Our method is quite different from that of (Ata, Harrison and Shepp (Ann. Appl. Probab. 15, 1145–1160, 2005)). To obtain a solution to the corresponding Hamilton–Jacobi–Bellman (HJB) equation, we analyze a family of ordinary differential
equations. We make use of some specific characteristics of this family of solutions to obtain the optimal buffer size b
*>0.
A.P. Weerasinghe’s research supported by US Army Research Office grant W911NF0510032. |
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Keywords: | Stochastic control Ergodic control Dynamic scheduling Queueing systems Diffusion approximations Heavy traffic limits Optimal buffer size Hamilton– Jacobi– Bellman equations |
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