An Adaptive Congestion Control Method for Guaranteeing Queuing Delay in RED-Based Queue Using Learning Automata

Some applications such as audio and video conferencing require a network to provide QoS guarantee. End-2-end delay is one of the prominent factors in QoS. Packets after crossing the routers queue arrive to destination node. Thus with guaranteeing the queuing delay in routers the network will be able to guarantee end-2-end delay. Furthermore developers can contract service level agreement (SLA) intelligently. In order to guarantee queuing delay, congestion control algorithms can be used in routers. Furthermore providers can contract service level agreement (SLA) intelligently. Congestion control algorithms are a solution to guarantee queuing delay. Random early detection (RED) is the most known and applicable congestion control algorithm. Although extended researches in this field were carried out, but there is no investigation on queuing delay guarantee in RED based queue yet. To achieve this goal, in this paper a novel method using stochastic learning automata is proposed. In the proposed method, thresholds of RED algorithm, in order to guarantee delay, in opposite to previous approaches are adjusted dynamically. Proposed method, in addition the delay guaranteeing, can increase the utilization of output link. Therefore, the proposed method can provide better QoS.