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A unified congestion control framework for diverse application preferences and network conditions

Published: 03 December 2021 Publication History

Abstract

With the increase of diversity in application needs and networks, existing congestion control algorithms (CCAs) do not accommodate this complicated reality. Previous classic CCAs are designed for a specific domain with fixed rules, failing to adapt to such diversities. Recently surged learning-based CCAs have great potential in adaptability and flexibility but are not practical due to unsatisfying performance on convergence, fairness, overhead and safety assurance. In this paper, we propose Libra, a unified congestion control framework, which empowers flexibility, adaptability, and practicality, by combining the wisdom of classic and reinforcement learning (RL)-based CCAs. Extensive evaluation of Libra's Linux kernel implementations on both live Internet and emulated networks shows performance improvement under dynamic networks (e.g., 1.2x throughput than Orca on average). At the same time, Libra can flexibly satisfy different application needs, reduce the running overhead by at most 0.92x and perform good fairness and convergence properties, well-fitting our theoretical analysis.

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      cover image ACM Conferences
      CoNEXT '21: Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies
      December 2021
      507 pages
      ISBN:9781450390989
      DOI:10.1145/3485983
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      Published: 03 December 2021

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      Author Tags

      1. TCP
      2. congestion control
      3. deep reinforcement learning

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