BLLB: a novel traffic policing mechanism for ATM networks

The statistical multiplexing of sources with diverse traffic characteristics in ATM networks necessitates the use of some policing mechanisms, especially sources 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. Even the leaky bucket control mechanism, which is the most widely and only implemented one has some drawbacks. In this paper anew policing mechanism that we named as Buffered Learning Leaky Bucket (BLLB) is developed and compared with the leaky bucket and other recent mechanisms. As will be shown by the simulation results, the new BLLB mechanism results in more statistical gain, guarantee of the QoS, and smart decisions for contract-non-violating sources