Congestion avoidance using enhanced RED with queueing delay (ERQD) algorithm in wired networks

A learning automata (LA) is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. here, we present a ERQD algorithm for congestion avoidance in wired networks. The main aim of this algorithm is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue and also reduces the Queue delay. We achieve this by applying the LA algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. In Every Iteration the ERQD, chooses the action which is having the highest estimate vector. This algorithm reduces the number of packet losses at the gateway and also reduces the queue delay.