A new traffic model and statistical admission control algorithm for providing QoS guarantees to on-line traffic

On-line traffic, including conversational calls, videoconference calls, and live video, is becoming an important type of traffic in the Internet. The traffic traces of on-line traffic are not pre-recorded, which means little information on the on-line traffic is known in advance. Hence, on-line traffic is hard to characterize by existing traffic models, such as D-BIND. In order to anticipate and capture the burstiness property of on-line traffic, we introduce a new confidence-level-based statistical bounding interval-length dependent (S-BIND) traffic model and a statistical admission control algorithm, based on the S-BIND traffic model: the GammaH-BIND algorithm. Our simulation results show that by using the S-BIND traffic model as inputs, the GammaH-BIND algorithm can achieve the maximum valid network utilization for both low-bursty and high-bursty on-line traffic, which is 50%/spl sim/70% higher than the achievable network utilization under the D-BIND traffic model.