The extended alternating fractal renewal process for modeling traffic in high-speed communication networks

Extensive studies indicate that traffic in highspeed communication networks exhibits long-range dependence (LRD) and impulsiveness, thus posing new challenges in network engineering. While many models have recently appeared for capturing the traffic LRD, far less models exist that account for impulsiveness as well as LRD. One of the few existing constructive models for network traffic is the celebrated On/Off model, or Alternating Fractal Renewal Process (AFRP). However, while the AFRP results in aggregated traffic with LRD, it fails to capture impulsiveness, yielding traffic with Gaussian marginal distribution. A new constructive model, namely the Extended AFRP (EAFRP), is proposed here, which overcomes the limitations of the AFRP model. We show that, for both single-user and aggregated traffic, it results in impulsiveness and long-range dependence, the LRD being defined here in a generalized sense. We provide queueing analysis of the proposed model, which clearly demonstrates the implications of the impulsiveness in traffic engineering, and validate all theoretical findings based on real traffic data.