Efficient LALRED for Congestion Avoidance Using Automata-Like Solution

Abstract

For ELALRED algorithm the concept of a Learning Automata-Like (LAL) mechanism devised for congestion avoidance in wired networks. The algorithm, named as Efficient LAL Random Early Detection (ELALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with an LAL philosophy. The primary objective of ELALRED 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. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In ELALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. ELALRED approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of ELALRED over the LALRED and traditional RED methods which were chosen as the benchmarks for performance comparison purposes.