Trading link utilization for queueing delays: an adaptive approach

Understanding the relationship between queueing delays and link utilization for general traffic conditions is an important open problem in networking research. Difficulties in understanding this relationship stem from the fact that it depends on the complex nature of arriving traffic and the problems associated with modelling such traffic. Existing AQM schemes achieve a "low delay" and "high utilization" by responding early to congestion without considering the exact relationship between delay and utilization. However, in the context of exploiting the delay/utilization tradeoff, the optimal choice of a queueing scheme's control parameter depends on the cost associated with the relative importance of queueing delay and utilization. The optimal choice of control parameter is the one that maximizes a benefit that can be defined as the difference between utilization and cost associated with queuing delay. We present two practical algorithms, Optimal Drop-Tail (ODT) and Optimal BLUE (OB), that are designed with a common performance goal: namely, maximizing this benefit. Their novelty lies in fact that they maximize the benefit in an online manner, without requiring knowledge of the traffic conditions, specific delay-utilization models, nor do they require complex parameter estimation. Packet level ns2 simulations are given to demonstrate the efficacy of the proposed algorithms and the framework in which they are designed