An Intelligent Traffic Regulator Scheme in Multimedia Networks
This paper proposed an intelligent and regulative traffic method for solving congestion control problems in multimedia networks. The proposed scheme, which is capable of rate-based predictive control, consists of two sub-systems: a long-term policy critic and a short-term rate-selector. Each controller in multimedia networks jointly learns the control policy by real-time interactions without prior knowledge of a network model. In addition, a reward evaluator provides reinforcement signals based on game theory to train controllers to adapt to fluctuant network environment. The well-trained controllers can take actions correctly to regulate source flow to simultaneously meet the requirements of high link utilization, low packet loss rate (PLR) and packet delay. Simulation results show that the proposed approach is effectively in controlling congestion of the multimedia traffic in internet networks.
KeywordsCongestion control Reinforcement signals
The authors greatly appreciate to the support of the National Science Council, R.O.C., under the Grant no. NSC 101-2632-E-230-001-NY3.
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