Statistical detection of congestion in routers

Detection of congestion plays a key role in numerous networking protocols, including those driving Active Queue Management (AQM) methods used in congestion control in Internet routers. This paper exploits the rich theory of statistical detection theory to develop simple detection mechanisms that can further enhance current AQM methods. The detection of congestion is performed using a Maximum Likelihood Ratio Test (MLRT) that is an asymptotically powerful unbiased test. The MLRT indicates that the likelihood of congestion grows super exponentially with the queue occupancy level. Performance evaluation of the likelihood detector shows it is robust to variations of the network parameters. The mathematical expression of the likelihood of congestion depends only on the current dropping rate, a desired queue occupancy level and the current queue occupancy. When incorporated into REM and PI, the MLRTbased detection improved the reaction time by at least 30%.

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Ivan D. Barrera, Stephan Bohacek, Gonzalo R. Arce
• Keywords: Artificial Intelligence, Detection, Queue Occupancy, Queue Occupancy Level, TSP 2010