TY - JOUR

T1 - Near-optimal control policy for loss networks

AU - Ku, Cheng-Yuan

AU - Yen, David C.

AU - Chang, I. Chiu

AU - Huang, Shi Ming

AU - Jordan, Scott

PY - 2006/8

Y1 - 2006/8

N2 - In this paper, the phenomenon of the optimal management of requests of service in general networks is formulated as a control problem for a finite number of multiserver loss queues with Markovian routing. This type of problem may arise in a wide range of fields, e.g., manufacturing industries, storage facilities, computer networks, and communication systems. Using inductive approach of dynamic programming, the optimal admission control can be induced to be the functions of the number of requested service in progress. However, for large-scale network, the computational burden to find optimal control policy may be infeasible due to its involvement of the states for all stations in the networks. Hence, the idea of bottleneck modeling is borrowed to compute the near-optimal admission control policy. We reduced the scale of loss network and decreased the difference between the original and reduced models by making compensation for system parameters. A novel method is proposed in this paper to compute the compensation. Numerical results show that the near-optimal control policy demonstrates close performance to the optimal policy.

AB - In this paper, the phenomenon of the optimal management of requests of service in general networks is formulated as a control problem for a finite number of multiserver loss queues with Markovian routing. This type of problem may arise in a wide range of fields, e.g., manufacturing industries, storage facilities, computer networks, and communication systems. Using inductive approach of dynamic programming, the optimal admission control can be induced to be the functions of the number of requested service in progress. However, for large-scale network, the computational burden to find optimal control policy may be infeasible due to its involvement of the states for all stations in the networks. Hence, the idea of bottleneck modeling is borrowed to compute the near-optimal admission control policy. We reduced the scale of loss network and decreased the difference between the original and reduced models by making compensation for system parameters. A novel method is proposed in this paper to compute the compensation. Numerical results show that the near-optimal control policy demonstrates close performance to the optimal policy.

KW - Discounted dynamic programming

KW - Downsizing approximation

KW - Loss queueing network

KW - Markov chain

KW - Near-optimal control policy

UR - http://www.scopus.com/inward/record.url?scp=27844587758&partnerID=8YFLogxK

U2 - 10.1016/j.omega.2005.01.002

DO - 10.1016/j.omega.2005.01.002

M3 - Article

AN - SCOPUS:27844587758

SN - 0305-0483

VL - 34

SP - 406

EP - 416

JO - Omega

JF - Omega

IS - 4

ER -