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AAC: Adaptively Adjusting Concurrency by Exploiting Path Diversity in Datacenter Networks

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Abstract

Recent datacenter load balancing designs make full use of all available multiple paths to achieve high bisection bandwidth and support the increasing traffic demands. However, a multitude of uncertainties, such as congestion and asymmetry, easily leads to long tailed latencies for unlucky flows on bad paths. In this paper, we aim at adjusting the maximum number of multiple paths used by existing load balancing designs to achieve good tradeoff between the tailed latency and link utilization. Specifically, we propose a packet-level load balancing called scheme Adaptively Adjusting Concurrency (AAC) to spread packets across the multiple paths, which are adaptively selected according to path diversity. AAC is deployed at switch, without any modifications on end-hosts. The experimental results of NS2 simulation and Mininet implementation show that AAC significantly reduces the flow completion time by \(\sim\)21–56% over the state-of-the-art datacenter load balancing designs including MPTCP, LetFlow and RPS.

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Acknowledgements

This work is supported by the Natural Science Foundation of Hunan Province, China (No. 2018JJ2084), National Natural Science Foundation of China (No. 61872387), and Project of Foreign Cultural and Educational Expert (No. G20190018003).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Weimin Gao, Jiawei Huang, Shaojun Zou, Weihe Li & Jianxin Wang

  2. Department of Computer and Information Science, Hunan Institute of Technology, Hengyang, 421002, China

    Weimin Gao

  3. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA

    Jianer Chen

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  1. Weimin Gao
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  2. Jiawei Huang
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  3. Shaojun Zou
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  4. Weihe Li
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Correspondence to Jiawei Huang.

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Gao, W., Huang, J., Zou, S. et al. AAC: Adaptively Adjusting Concurrency by Exploiting Path Diversity in Datacenter Networks. J Netw Syst Manage 29, 26 (2021). https://doi.org/10.1007/s10922-021-09590-z

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