Congestion Prediction of Self-Similar Network through Parameter Estimation

In a state of emergency, in complex and dynamic situations where packet delay increases and congestion builds up, certain network nodes may not to able to handle the traffic load. To avoid the congestion build-up in advance, it is mission critical to predict the symptoms of network traffic and to preemptively alter the routing paths to allow a smooth and efficient flow of data packets for effective situation management. In this paper, we have developed a practical methodology for estimation of key parameters of self-similar network traffic using index of dispersion for counts and coefficient of determination. Self-similarity causes performance degradation in the queueing delay and buffer overflow at routers and at switches. We proved the impact of Hurst parameter and fractal onset time on the average queueing delay and the waiting-time distribution of self-similar traffic, utilizing experimental queueing analysis. Based on the understanding obtained, we can predict the congestion of data network in advance through estimation of traffic parameters. In addition, the results of this study on the delay provide a practical means of finding a lower delay path in data networks under the self-similarity