Scalable in-network rate monitoring

We propose a highly scalable statistical method for modelling the monitored traffic rate in a network node and suggest a simple method for detecting increased risk of congestion at different monitoring time scales. The approach is based on parameter estimation of a lognormal distribution using the method of moments. The proposed method is computationally efficient and requires only two counters for updating the parameter estimates between consecutive inspections. Evaluation using a naive congestion detector with a success rate of over 98% indicates that our model can be used to detect episodes of high congestion risk at 0.3 s using estimates captured at 5 m intervals.