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Elephants Sharing the Highway: Studying TCP Fairness in Large Transfers over High Throughput Links

Published: 12 November 2023 Publication History

Abstract

Escalating bandwidth demand strains high-performance data networks, posing potential performance risks. TCP congestion control algorithms enhance reliability and optimize bandwidth usage. Network performance is influenced by factors such as AQM algorithms and router buffer size. In the context of constrained network resources, understanding how TCP flows share networks and the resulting performance impact is essential.
This paper introduces insights into TCP fairness and performance involving a comparison of TCP CUBIC, Reno, Hamilton, and BBR versions 1 and 2 across real-world networks supporting high bandwidths of up to 25 Gbps. The research explores TCP behaviors with AQM algorithms like FIFO, FQ_CODEL, and RED, alongside diverse buffer sizes. Notably, findings reveal that manipulating buffers and queuing methods yields contrasting outcomes based on bandwidth. BBRv2 emerges as a superior fair algorithm, pivotal for swift transfers, particularly in scientific data scenarios. These results provide crucial guidance for future network design, ensuring equitable performance optimization.

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        cover image ACM Other conferences
        SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
        November 2023
        2180 pages
        ISBN:9798400707858
        DOI:10.1145/3624062
        This work is licensed under a Creative Commons Attribution International 4.0 License.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 12 November 2023

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        Author Tags

        1. Active Queue Management
        2. Buffer Size
        3. Elephant Flows
        4. FABRIC
        5. Fairness
        6. High-Bandwidth
        7. High-Speed Internet
        8. TCP Congestion Control

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        • Department of Energy under the Integrated Computational and Data Infrastructure (ICDI) for Scientific Discovery

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