Modeling VBR traffic with autoregressive Gaussian processes

Abstract

Previous studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.