高技术通讯2025，Vol.35Issue(7)：711-723,13.DOI:10.3772/j.issn.1002-0470.2025.07.004
pBBR:面向应用性能偏好的帕累托最优拥塞控制机制
pBBR:Pareto-optimal congestion control for application performance preference
摘要
Abstract
As the core of network transmission control mechanism,congestion control focuses on how to optimize specific transmission performance targets in heterogeneous network environments.Existing congestion control mechanisms ignore the Pareto optimal frontier(POF)distribution of performance preferences of different applications in the two dimensions of throughput and delay,making it difficult to meet the performance requirements of differentiated appli-cations.To mitigate the problem above,this paper proposes a Pareto-optimal congestion control mechanism for ap-plication performance preferences,Pareto-optimal BBR(pBBR),which combines the ideas of offline network learn-ing and online control parameter optimization to satisfy applications' preference to the greatest extent.Experimental results show that pBBR can determine the switching of network scenarios within a collection-identification cycle,thereby quickly selecting the optimal parameters for current scenario.In each network scenario,pBBR can maximize the satisfaction of different application performance preferences:for throughput-priority services,pBBR can reach 97％of Cubic(throughput-first),and the latency is only 52％of Cubic;for latency priority services,the latency of pBBR can reach 95％of Sprout(latency-first),while the throughput loss is only 1％.Furthermore,multi-parameter optimization can enhance the performance of pBBR.For example,in high-speed railway LTE scenarios,the through-put and latency of one-parameter pBBR reach 94％and 99％of Cubic,respectively,while the three-parameter pB-BR improves these metrics to 101％and 93％(outperforming Cubic).关键词
拥塞控制/吞吐量-时延/帕累托最优/贝叶斯参数优化Key words
congestion control/throughput-latency/Pareto optimum/Bayesian parameter optimization引用本文复制引用
钟植任,潘恒,武庆华,谢高岗..pBBR:面向应用性能偏好的帕累托最优拥塞控制机制[J].高技术通讯,2025,35(7):711-723,13.基金项目
国家重点研发计划(2022YFB2901800)和国家自然科学基金(62102397)资助项目. （2022YFB2901800）
