A robust packet scheduling algorithm for proportional delay differentiation services

Abstract

Proportional delay differentiation (PDD) model is an important approach to relative differentiated services provisioning on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing PDD packet scheduling algorithms are able to achieve the goal in long time-scales when the system is highly utilized. This paper presents a new PDD scheduling algorithm, called Little’s average delay (LAD), based on a proof of Little’s Law. It monitors the arrival rate of the packets in each traffic class and the cumulative delays of the packets and schedules the packet according to their transient queueing properties in order to achieve the desired class delay ratios in both short and long time-scales. Simulation results show that LAD is able to provide predictable and controllable services in various system conditions and that such services, whenever feasible, can be guaranteed, independent of the distributions of packet arrivals and sizes. In comparison with other PDD scheduling algorithms, LAD can provide the same level of service quality in long time-scales and more accurate and robust control over the delay ratio in short time-scales. In particular, LAD outperforms its main competitors significantly when the desired delay ratio is large.