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Controlled and optimally controlled multiplexing systems: A numerical exploration
Authors:Harold J Kushner  Jichuan Yang  Dennis Jarvis
Institution:(1) Division of Applied Mathematics, Brown University, 02912 Providence, RI, USA
Abstract:Large controlled multiplexing systems are approximated by diffusion type processes yielding a very efficient way of approximation and good numerical methods. The ldquolimitrdquo equations are an efficient aggregation of the original system, and provide the basis of the actual numerical approximation to the control problem. The numerical approximations have the structure of the original problem, but are generally much simpler. The control can occur in a variety of places; e.g., ldquoleaky bucketrdquo controllers, control of ldquomarked cellsrdquo at the transmitter buffer, or control of the transmitter speed. From the point of view of the limit equations, those are equivalent. Various forms of the optimal control problem are explored, where the main aim is to control or balance the losses at the control with those due to buffer overflow. It is shown that much can be saved via the use of optimal controls or reasonable approximations to them. We discuss systems with one to three classes of sources, various aggregation methods and control approximation schemes. There are qualitative comparisons of various systems with and without control and a discussion of the variations of control and performance as the systems data and control bounds vary. The approach is a very useful tool for providing both qualitative and quantitative information which would be hard to get otherwise. The results have applications to various forms of the ATM and broadband integrated data networks.The work was partially supported by AFOSR-91-0375 and (AFOSR) F49620-92-J-088-1DEF.The work was partially supported by grants (AFOSR) F49620-92-J-008-1DEF, AFOSR-91-03750.This work was partially supported by DAAH04-93-0070 (ARO) and AFOSR-91-0375.
Keywords:Multiplexing systems  ATM systems  controlled queues  numerical methods in stochastic control  Markov chain approximations  reflected diffusions  optimal stochastic control
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