Robust Constraint-Aware Bayesian Tuning of BBRv2 for QUIC under Tactile Internet Constraints

2026-08-14

AI Summary

This study addresses the challenge of balancing low latency and high throughput in QUIC-based Tactile Internet transmission by proposing a robust constraint-aware tuning framework. The approach formulates BBRv2 parameter configuration as a constrained black-box optimization problem, employing Tree-structured Parzen Estimator (TPE) Bayesian optimization combined with multi-scenario simulations for efficient search. Experimental results demonstrate that this framework strictly guarantees tail latency and packet loss rate constraints under diverse network impairments while significantly reducing jitter. Furthermore, it maintains competitive throughput levels, effectively achieving a robust trade-off among multiple performance metrics to satisfy the stringent Quality of Service requirements inherent to the Tactile Internet.

Abstract

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.

Problem

• Tactile Internet
• BBRv2
• QUIC
• Congestion Control
• Latency

Innovation

• Bayesian Optimization
• Constraint-Aware Tuning
• BBRv2
• QUIC
• Tactile Internet