A case for exploiting self-similarity of network traffic in TCP congestion control

Analytical and empirical studies have shown that self-similar traffic can have a detrimental impact on network performance including amplified queuing delay and packet loss ratio. On the flip side, the ubiquity of scale-invariant burstiness observed across diverse networking contexts can be exploited to design better resource control algorithms. We explore the issue of exploiting the self-similar characteristics of network traffic in TCP congestion control. We show that the correlation structure present in long-range dependent traffic can be detected on-line and used to predict future traffic. We then devise an novel scheme, called TCP with traffic prediction (TCP-TP), that exploits the prediction result to infer, in the context of AIMD (additive increase, multiplicative decrease) steady-state dynamics, the optimal operational point for a TCP connection. Through analytical reasoning, we show that the impact of prediction errors on fairness is minimal. We also conduct ns-2 simulation and FreeBSD 4.1-based implementation studies to validate the design and to demonstrate the performance improvement in terms of packet loss ratio and throughput attained by connections.