Abstract
The statistical multiplexing of sources with diverse traffic characteristics in ATM networks leads to serious congestion control problems. One of the most fundamental is the policing of sources, particularly those with bursty traffic characteristics. Because of the statistical nature of burstiness, the policing of these sources is difficult and the known policing mechanisms cannot control them effectively. In this paper, the Leaky Bucket mechanism is enhanced using a learning algorithm in order to “learn” the behaviour of the source. As will be shown in the simulation results, the tighter and faster control achieved by the proposed methodology results in more statistical gain and better guarantee of the QoS constraints.
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