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Fast and isolation guaranteed coflow scheduling via traffic forecasting in multi-tenant environment

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Abstract

It is a challenging task to achieve the minimum average CCT (coflow completion time) and provide isolation guarantees in multi-tenant datacenters without prior knowledge of coflow sizes. State-of-the-art solutions either focus on minimizing the average CCT or providing optimal isolation guarantees. However, achieving the minimum average CCT and isolation guarantees in multi-tenant datacenters is difficult due to the conflicting nature of these objectives. Therefore, we propose FIGCS-TF (Fast and Isolation Guarantees Coflow Scheduling via Traffic Forecasting), a coflow scheduling algorithm that does not require prior knowledge. FIGCS-TF utilizes a lightweight forecasting module to predict the relative scheduling priority of coflows. Moreover, it employs the MDRF (monopolistic dominant resource fairness) strategy for bandwidth allocation, which is based on super-coflows and helps achieve long-term isolation. Through trace-driven simulations, FIGCS-TF demonstrate communication stages that are 1.12\(\times\), 1.99\(\times\), and 5.50\(\times\) faster than DRF (Dominant Resource Fairness), NCDRF (Non-Clairvoyant Dominant Resource Fairness) and Per-Flow Fairness, respectively. In comparison with the theoretically minimum CCT, FIGCS-TF experiences only a 46% increase in average CCT at the top 95th percentile of the dataset. Overall, FIGCS-TF exhibits superior performance in reducing average CCT compared to other algorithms.

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Data Availability

No datasets were generated or analysed during the current study.

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Funding

This study was supported by the Natural Science Foundation of Shandong Province, China (Grant No.ZR2022QF143), Natural Science Foundation of Shandong Province, China (Grant No.ZR2021QF130), Shaanxi key Laboratory of Information Communication Network and Security (Xi’an university of Posts and Telecommunications) open project (Grant No.ICNS202202), Hubei Key Laboratory of intelligent Robot (Wuhan Institute of Technology) open project (Grant No.HBIR202201), Wuhan knowledge innovation special project (Grant No.30106230186).

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Author notes

  1. Huyin Zhang, Fei Yang, Sheng Hao have contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Daxue Road, Jinan, 250000, Shandong, China

    Chenghao Li

  2. School of Computer Science, Wuhan University, Bayi Road, Wuhan, 430000, Hubei, China

    Huyin Zhang

  3. China North Communication Technology CO., LTD., Wuhan, 430000, Hubei, China

    Fei Yang

  4. School of Computer Science, Central China Normal University, Luoyu Road, Wuhan, 430000, Hubei, China

    Sheng Hao

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  1. Chenghao Li
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Contributions

C.L. wrote the main manuscript text and designed the model of the manuscript.H.Z., Y.F. and S.H. collected the data, performed the analysis.

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Correspondence to Chenghao Li.

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Li, C., Zhang, H., Yang, F. et al. Fast and isolation guaranteed coflow scheduling via traffic forecasting in multi-tenant environment. J Supercomput 80, 26726–26750 (2024). https://doi.org/10.1007/s11227-024-06457-3

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