Congestion Control for Self-Similar Network Traffic'

Analytical and empirical studies have shown that self-similar network traffic can have a detri-mental impact on network performance including amplified queueing delay and packet loss rate. Given the ubiquity of scale-invariant hurstincss observed across diverse networking contexts, finding effective traffic control algorithms capable of delecting and managing self-similar traffic ha'> become an important problem. In this paper, we study congeslion control algorithms for improving throughput under self-similar traffic conditions. Although scale-invariant burstiness imposes a limit on the ability to conjointly achieve high quality of service (QoS) and utilization, the long-range dependence associated with self-similar traffic leaves open the possibility that the correlation structure may be exploited for performance enhancement purposes. We use a form of predictive congestion control called Selective Aggressiveness Control (SAC) to show that long-range dependence can be on-line detected and exploited to improve throughput. ""e show that the correlation structure present in long-range dependent traffic can be used to predict future traffic levels over time scales that are relevant to feedback congestion control. This on-lin