SACWOM: Synergistic Adaptive Congestion Window Optimization Mechanism for Self-Clocked Algorithm

Congestion Control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each faces limitations: BBR encounters difficulty in balancing responsiveness and fairness when rapid fluctuations in network conditions arise, which can lead to inefficient performance in shared environments. On the other hand, although SCReAM is designed for multimedia traffic, it struggles to dynamically adapt the congestion window to unpredictable or highly congested networks, leading to inefficient resource utilization. This paper proposes SACWOM, a hybrid congestion window optimization mechanism that combines our novel method with SCReAM’s rate control and BBR’s bandwidth-delay estimation to enhance throughput stability, fairness, and adaptability by dynamically adjusting the congestion window. Extensive simulations demonstrate SACWOM’s significant improvements over SCReAM in managing congestion window and bytes in flight under diverse network conditions. In Phase I, SACWOM achieved up to 11.96-13.27% increase in congestion window and bytes in flight by maintaining higher data flow. Phase II analysis shows up to 20.76-22.64% improvements with optimized configurations. Finally, Phase III, comprising 100 experiments, reveals SACWOM’s dynamic adaptability, achieving up to 50-70% improvements. These results highlight SACWOM as a robust mechanism suitable for various applications across diverse network scenarios.