Congestion control in ATM networks using learning algorithms

This paper describes a `real time´ solution to the link-by-link call admission control (AC) problem in ATM networks for bursty and variable bit rate video traffic and for mixes of them. The proposed method employs SELA, a novel Stochastic Estimator Learning Algorithm, for predicting whether a new call should be accepted or not. Call acceptance decision is derived from the independent two-call and cell-level execution of two distinct learning automata whose selected actions are combined via an AND function. The feedback which the algorithms receive has been drawn from efficient `equivalent bandwidth´ approximations and accurate cell loss probability estimations. This AC mechanism exhibits a remarkable gain obtained from statistical multiplexing, compared with other schemes reported in the literature