Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Closed-loop control with delayed information
SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Control of a random walk with noisy delayed information
Systems & Control Letters
Adaptive Markov Control Processes
Adaptive Markov Control Processes
Admission Control With Incomplete Information To A Finite Buffer Queue
Probability in the Engineering and Informational Sciences
Optimal admission control in a queueing system with heterogeneous traffic
Operations Research Letters
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We consider a problem of admission control to a single queue in discrete time. The controller has access to k-step old queue lengths only, where k can be arbitrary. The problem is motivated, in particular, by recent advances in high-speed networking where information delays have become prominent. We formulate the problem in the framework of Completely Observable Controlled Markov Chains, in terms of a multi-dimensional state variable. Exploiting the structure of the problem, we show that under appropriate conditions, the multi-dimensional Dynamic Programming Equation (DPE) can be reduced to a unidimensional one. We then provide simple computable upper and lower bounds to the optimal value function corresponding to the reduced unidimensional DPE. These upper and lower bounds, along with a certain relationship among the parameters of the problem, enable us to deduce partially the structural features of the optimal policy. Our approach enables us to recover simply, in part, the recent results of Altman and Stidham, who have shown that a multiple-threshold-type policy is optimal for this problem. Further, under the same relationship among the parameters of the problem, we provide easily computable upper bounds to the multiple thresholds and show the existence of simple relationships among these upper bounds. These relationships allow us to gain very useful insights into the nature of the optimal policy. In particular, the insights obtained are of great importance for the problem of actually computing an optimal policy because they reduce the search space enormously.