Using autoregressive Gaussian processes with trends and aggregations to model self-similar traffic
Recent measurement studies have revealed that multimedia network traffic has complex statistical characteristics. They have presented convincing evidence that the traffic exhibits self-similarity in nature. Generating self-similar traffic traces is increasingly important to research on characterization of self-similar processes and their impacts on network performance. However, self-similar traffic traces are difficult to be generated due to their long-range dependence. No matter fractional Gaussian noise model or fractional autoregessive integrated motion average model suffers from that computation efforts, they are proportional to the length of the traces that we want to generate. In this paper, we propose two new trace-generating schemes, whose computation efforts are independent of the length of the trace: autoregressive Gaussian processes with trends and autoregressive Gaussian processes with aggregations. The results show that it is effective to generate the long-range dependence with these two schemes. The autocorrelations and queuing performance of traces generated from these two approaches are close to those of the empirical trace. (C) 2002 Elsevier Science B.V. All rights reserved.
self-similar long-range dependence autoregressive gaussian processes with trends autoregressive gaussian processes with aggregations hurst effects