TCPLS

Rochet, Florentin; Assogba, Emery; Piraux, Maxime; Edeline, Korian; Donnet, Benoît; Bonaventure, Olivier

doi:10.1145/3485983.3494865

Cited by 5 publications

(1 citation statement)

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“…BFC [43] achieves near optimal throughput and tail latency behavior in datacenter by hop-by-hop control. TCPLS offers more control to the application by providing multiplexing, connection migration service in TCP [44]. Orca [20], Libra [45], TCP-Drinc [46] and [23] adjust CCAs' parameters (e.g.…”

Section: Related Workmentioning

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: Related Workmentioning

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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TCPLS

Rochet

Assogba

Piraux

et al. 2021

Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies

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TCP and TLS are among the essential protocols in today's Internet. TCP ensures reliable data delivery while TLS secures the data transfer. Although they are very often used together, they have been designed independently following the Internet layered model. This paper demonstrates the various benefits that a closer integration between TCP and TLS would bring.By leveraging the extensible TLS 1.3 records, we combine TCP and TLS into TCPLS to build modern transport services such as multiplexing, connection migration, stream steering, and bandwidth aggregation. These services do not modify the TCP wire format and are resistant to middleboxes. TCPLS offers a powerful API enabling applications to precisely express the required transport services, ranging from a single-path single-stream connection to a multistream connection over several network paths, enabling choices between aggregated bandwidth and head-of-line blocking avoidance.Compared to MPTCP, our TCPLS prototype offers more control to the application and can be easily deployed as an extension to user-space TLS libraries, while being implemented at a low cost. Measurements demonstrate that it offers higher performance than existing QUIC libraries with a super set of transport services. CCS CONCEPTS• Networks → Transport protocols; Network protocol design.

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