A Compensatory Packet Dropping Routine for Proportional Loss Rate Differentiation

Abstract

Service differentiation is one of the key issues in the current Internet. In this paper, we focus on a recent proposal for proportional loss rate differentiation which employs a single FIFO queue, an AQM algorithm for computing the packet drop probability, and a counter-based packet dropping routine for achieving the intended proportional loss rate differentiation among classes. It is first shown that, when the target dropping probability of a class is large, the counter-based packet dropping routine may yield a significant amount of error between the target and measured drop probabilities for the class, and subsequently, fails to maintain the loss rate ratios between classes as intended. To avoid this problem, a new compensatory packet dropping routine is developed in this paper. Then, a series of simulation experiments are conducted using the ns-2 simulator to assess the performances of the two dropping routines under various congestion conditions and quality spacings between classes. The simulation results show that, unlike the counter-based dropping routine, the proposed compensatory dropping routine is effective in keeping the loss rate ratios between classes closely on target regardless of the degree of congestion and quality spacing between classes, while the two dropping routines perform similarly in terms of throughput and queueing delay in the bottleneck link. In addition, such robustness of the proposed routine is achieved without any additional control parameter or computational effort compared to the counter-based routine.