DDM : Statistical Admission Control Using Delay Distribution Measurement

Measurement-based admission control algorithms exploit the statistical multiplexing nature of input traffic to admit more flows into a system than is possible when assuming that each admitted flow is fully loaded. However, most previous measurement-based admission control algorithms did not have a satisfactory solution to the problem of guaranteeing distinct per-flow delay bounds while exploiting statistical multiplexing. This paper presents a novel technique, called Delay Distribution Measurement (DDM) based admission control algorithm, which effectively exploits statistical multiplexing by dynamically measuring the distribution of the ratios between actual packet delay and worst-case delay bound. With this delay ratio distribution, DDM is able to gauge the actual load of a system, estimate the actual resource requirement of new flows with distinct delay bound requirements, and eventually determine whether to admit them. The DDM algorithm develops a novel quantitative framework to exploit the well known fact that the actual delay experienced by most packets of a real-time flow is usually far smaller than its worst-case delay bound requirement. This framework also provides the additional flexibility to support flows with probabilistic delay bound requirements, i.e., flows for which a certain percentage of delay bound violations is tolerable. Flows that can tolerate more delay bound violations can reserve less resource than those that tolerate less, even though they have the same delay bound requirement. A comprehensive simulation study using Voice over IP traces shows that, when compared to deterministic admission control algorithms, the DDM algorithm can potentially increase the number of admitted flows (and link utilization) by up to a factor of 3.0 when the delay violation probability is as small as 10−5.