A measurement-based congestion alarm for self-similar traffic

Abstract

Self-similar traffic is distinguished by positive correlation, which can be exploited for better traffic management. Inspired by measurement-based admission control schemes, a measurement-based congestion alarm is proposed. The aggregate traffic at an output port of a switch or router in a high-speed network is modeled by a fractional Gaussian noise process. Traffic measurements are performed in regular time intervals to determine the current traffic loading. This information is then used to predict the loading situation in the near future. If congestion is likely to occur, a congestion alarm is set off and appropriate network management functions taken to alleviate the possible congestion. The above constitutes a closed loop feedback control mechanism that maintains high resource utilization. Simulation results show that the proposed scheme, when used with dynamic bandwidth allocation, reduces bandwidth requirements by more than 20%.