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Congestion Management of Self Similar IP Traffic using Probability based Normal and Exponential marking RED

Schemes described in the literature on network congestion management are in general based on queue management. Also it is widely accepted that Poisson model is not sufficient to characterize the traffic in current Internet. In this paper, we present the details of some simulation studies carried out on an alternate RED (random early detection) algorithm for traffic congestion management in IP networks having self similar input. We first discuss the basic scheme of normal as proposed by Floyd et al., for the Poisson input model, and then explain a new AQM proposed by us. Our modification to the RED algorithm takes into consideration probability values corresponding to the average queue lengths for computing the marking/dropping probability. Verification of the algorithms proposed vis-a-vis that of Floyd as well as the ones proposed by us, has been done by simulating self-similar traffic. Results of the verification have been discussed in the paper