H-BIND: A New Approach to Providing Statistical Performance Guarantees to VBR Traffic
Current solutions to providing statistical performance guarantees to bursty tra c such as compressed video encounter several problems: 1) source tra c descriptors are often too simple to capture the burstiness and important time-correlations of VBR sources or too complex to be used for admission control algorithms; 2) stochastic descriptions of a source are inherently di cult for the network to enforce or police; 3) multiplexing inside the network's queues may change the stochastic properties of the source in an intractable way, precluding the provision of end-toend QoS guarantees to heterogeneous sources with di erent performance requirements. In this paper, we present a new approach to providing end-to-end statistical performance guarantees that overcomes these limitations. We term the approach Hybrid Bounding Interval Dependent (H-BIND) because it uses the Deterministic-BIND tra c model to capture the correlation structure and burstiness properties of a stream; but unlike a deterministic performance guarantee, it achieves a Statistical Multiplexing Gain (SMG) by exploiting the statistical properties of deterministicallybounded streams. Using traces of MPEG-compressed video, we show that the H-BIND scheme can achieve average network utilizations of up to 86% in a realistic scenario.