Shifting-level process as a LRD video traffic model and related queuing results

摘要

Recently is has been reported that variable bit rate (VBR) video traffic exhibits long-range dependence (LRD). Various processes have been proposed for modeling traffic with LRD and analyzing its effects on network performance. However, in the previous models it is not possible to identify the effects of short- and long-term correlation of video traffic on queuing performance, and thus many seemingly contradictory arguments on the importance of LRD in VBR video traffic can be found in the literature. In this paper, we present a video traffic model based on the shifting-level (SL) process. We observe that the autocorrelation function (ACF) of an empirical video trace is accurately captured by a shifting-level process with compound correlation (SLCC): an exponential decay for small lags and a hyperbolic one for large lags. Especially, we present a parameter matching algorithm for video traffic. The continuous-time first-order discrete auto-regressive (C-DAR(10)) model, which is a short-range dependent (SRD) video traffic model, can be considered a kind of SLCC process with an exponential correlation term only. Thus, comparing the queuing performances of the C-DAR(1) model and the SLCC with that of a real video trace, it is possible to identify the effects of SRD and LRD in VBR video traffic on queuing performance. From simulation results, we find that LRD may have a significant effect on queuing behavior under heavy traffic loads and large buffer conditions.