Sared: Self-adaptive active queue management scheme for improving quality of service in network systems

Abstract

Considering the phenomenal growth of network systems, congestion remains a threat to the quality of the service provided in such systems; hence, research on congestion control is still relevant. The Internet research community regards active queue management (AQM) as an effective approach for addressing congestion in network systems. Most of the existing AQM schemes possess static drop patterns and lack a self-adaptation mechanism; as such they do not work well for networks where the traffic load fluctuates. This paper proposes a selfadaptive random early detection (SARED) scheme that smartly adapts its drop pattern based on a current network’s traffic load in order to maintain improved and stable performance. Under light-to moderate-load conditions, SARED operates in nonlinear modes in order to maximize utilization and throughput, while it switches to a linear mode in order to avoid forced drops and congestion under high-load conditions. Our conducted experiments revealed that SARED provides optimal performance regardless of the condition of the traffic load.