Optimal Statistical Tuning of the RED Parameters

Achieving minimal loss while satisfying an acceptable delay profile remains to be an open problem under the RED queuing discipline. In this paper, we present a framework targeted at optimal fine tuning of the RED parameters in order to address such problem. For a given traffic pattern and utilizing a statistical analysis of finite-state Markov chains, we formulate an optimization problem aimed at addressing the loss and delay tradeoff of the RED queuing discipline. Our two-step iterative solution to the problem identifies the optimal settings of the RED parameters. We prove the convergence of our solution and investigate its low complexity characteristics. We apply our framework to a number of generic queuing and TCP scenarios in order to capture loss and delay performance of our algorithms versus buffer capacity and service rate. Based on our results, we argue that our model is capable of optimally addressing the loss-delay tradeoff of RED queues accommodating time-varying traffic profiles.