Multiple time scale congestion control for self-similar network traffic

Abstract

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 rate. Given the ubiquity of scale-invariant burstiness observed across diverse networking contexts, finding effective traffic control algorithms capable of detecting and managing self-similar traffic has become an important problem. In this paper, we study congestion control algorithms for improving network performance — in particular, throughput — under self-similar traffic conditions. Although scale-invariant burstiness implies the existence of concentrated periods of contention and idleness, the long-range dependence associated with self-similar traffic leaves open the possibility that correlation structure at larger time scales may be exploited for performance enhancement purposes. We construct a 2-level multiple time scale congestion control protocol that exercises congestion control concurrently across two time scales an order of magnitude apart. The first component — acting at the smaller time scale — is a generic linear increase/exponential decrease feedback congestion control that uses implicit prediction afforded by feedback to affect rate control sensitive to changes in network state at 20–200 ms time scales. The second component — acting at 2–5 s time scales — uses explicit prediction to detect persistent shifts in overall network contention and uses this information to modulate the aggressiveness exhibited by the first component. We show that cooperative interaction between the two congestion control modules acting on information at different time scales leads to improved performance vis-à-vis the case when the large time scale component is absent. We show that the improvement factor increases with long-range dependence and we show that as the number of flows engaging in multiple time scale congestion control (MTSC) increases, both fairness and efficiency are preserved.