Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms

Zhou, Jianer; Qiu, Xinyi; Li, Zhenyu; Tyson, Gareth; Li, Qing; Duan, Jingpu; Yi, Wang

doi:10.1109/icnp52444.2021.9651912

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…While the basic idea of Antelope has been introduced in [26], this extended version adds two new enhancements to Antelope: (i) the incorporation of applications' performance preferences and the remote cloud-based learning; and (ii) several sets of new experiments.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms

Zhou

Qiu

et al. 2023

IEEE/ACM Trans. Networking

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Most congestion control algorithms (CCAs) are designed for specific network environments. As such, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising a one-size-fitsall algorithm (which is a likely impossible task), we propose a system to dynamically switch between the most suitable CCAs for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure the stack to use the most suitable CCA for individual flows. We build a machine learning model to learn which algorithm works best for individual conditions and implement kernellevel support for dynamically switching between CCAs. The framework also takes application requirements of performance into consideration to fine-tune the selection based on applicationlayer needs. Moreover, to reduce the overhead introduced by machine learning on individual front-end servers, we (optionally) implement the CCA selection process in the cloud, which allows the share of models and the selection among front-end servers. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. The results demonstrate the effectiveness of Antelope via dynamic adjusting the CCAs for individual flows. Specifically, Antelope achieves an average 16% improvement in throughput compared with BBR, and an average 19% improvement in throughput and 10% reduction in delay compared with CUBIC.

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Section: Introductionmentioning

confidence: 99%