STOCHASTIC ESTIMATOR LEARNING AUTOMATON BASED APPROACH IN REDUCING CONGESTION LEVEL IN ATM NETWORK

摘要

The performance difference between setting explicit rate deterministically for transmitting ABR sources and doing the same stochastically using a learning automaton is of particular interest The performance difference is measured by comparing the congestion levels of the Stochastic Estimator Learning Automaton (SELA) based control scheme with the reference deterministic mechanism. This paper highlight the improved performance envisaged by using Stochastic Estimator Learning Automaton (SELA) approach in reducing congestion level in ATM network Simulation results show that the stochastic estimator gives a better performance. The result also shows that the higher average congestion level experienced by the conventional deterministic approach is mainly due to the propagation time delay in the closed-loop feedback control schemes.