HydraCC: Finding the Pareto Frontiers of Congestion Control via Multi-objective Evolutionary Exploration

Current congestion control algorithms often fall short when balancing multiple objectives, leading to inefficient competition and subsequently diminishes network efficiency. To address this challenge, we introduce HydraCC, an innovative multi-objective congestion control solution. Through theoretical derivation, we elucidate the impact of algorithm parameters on several performance metrics, such as QoS fairness, responsiveness, throughput, and window fluctuation. Our analysis reveals that these objectives often correlate or even compete with each other. Building on this insight, HydraCC is designed to expand a Pareto solution set from an initial solution. Specifically, using a carefully architected method based on Krylov subspace iteration, we efficiently explore the evolutionary direction of Pareto solutions. Crucially, each solution presents its own strengths and costs, thereby offering a broader space for trade-offs. Through rigorous real-world experiments and simulations, we demonstrate HydraCC's adaptability, and its superiority to existing state-of-the-art congestion control algorithms across various performance metrics. Moreover, it significantly mitigates the issues associated with multiple coexisting flows. When compared to contemporary leading algorithms such as Orca, Cubic, and PCC-Vivace, the convergence stability of HydraCC shows an improvement in the range of 47.89 77.21%.