AI 中文总结
针对触觉互联网对QUIC传输的严格要求，本文提出鲁棒约束感知的BBRv2贝叶斯调优框架，实验表明该框架可在保持吞吐量的同时改善延迟、抖动和丢包性能。
AI 中文摘要
触觉互联网应用对延迟、抖动、丢包率和响应性提出了严格要求，这使得传输配置成为关键设计因素。尽管BBRv2提供了基于模型的拥塞控制框架，具有强大的吞吐量潜力，但其默认行为可能与延迟敏感的交互场景适配不佳。本文提出了一种面向QUIC中BBRv2的鲁棒且感知约束的调优框架，其中参数选择被表述为在多种仿真网络条件下的昂贵黑盒优化问题。调优过程采用基于树结构Parzen估计器的贝叶斯优化，在带噪声的实验测量下高效探索有界参数空间。目标被设计为在保持吞吐量的同时约束尾部延迟和丢包率，延迟不稳定性则通过抖动指标单独评估。在低、中、高损伤场景下的实验结果表明，调优后的配置在保持与标准QUIC拥塞控制基线相当的有效吞吐量的同时，改善了尾部延迟、抖动行为和丢包性能。这些结果支持鲁棒黑盒调优作为使QUIC传输行为适配触觉互联网风格需求的实用方法。
英文摘要
Tactile Internet applications place strict require- ments on latency, jitter, loss, and responsiveness, which makes transport configuration a critical design factor. Although BBRv2 offers a model-based congestion control framework with strong throughput potential, its default behavior may not be well aligned with delay-sensitive interactive scenarios. This paper presents a robust and constraint-aware tuning framework for BBRv2 in QUIC, where parameter selection is formulated as an expensive black-box optimization problem over multiple emulated network conditions. The tuning process uses Bayesian optimization with the Tree Structured Parzen Estimator to efficiently explore a bounded parameter space under noisy experimental measure- ments. The objective is designed to preserve throughput while enforcing limits on tail latency and loss, while delay instability is evaluated separately through the jitter metric. Experimental results across low, medium, and high impairment scenarios show that the tuned configuration improves tail latency, jitter behavior, and loss performance while maintaining competitive goodput relative to standard QUIC congestion control baselines. These results support robust black-box tuning as a practical method for adapting QUIC transport behavior to tactile Internet style requirements.
