H-BIND: a new approach to providing statistical performance guarantees to VBR traffic

Current solutions to providing statistical performance guarantees to bursty traffic such as compressed video encounter several problems: (1) source traffic 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 difficult 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-to-end QoS guarantees to heterogeneous sources with different performance requirements. 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 traffic 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 deterministically-bounded 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.