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Online Runtime Environment Prediction for Complex Colocation Interference in Distributed Streaming Processing

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14074))

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Abstract

To improve system resource utilization, multiple operators are co-located in the distributed stream processing systems. In the colocation scenarios, the node runtime environment and co-located operators affect each other. The existing methods mainly study the impact of the runtime environment on operator performance. However, there is still a lack of in-depth research on the interference of operator colocation to the runtime environment. It will lead to inaccurate prediction of the performance of the co-located operators, and further affect the effect of operator placement. To solve these problems, we propose an online runtime environment prediction method based on the operator portraits for complex colocation interference. The experimental results show that compared with the existing works, our method can not only accurately predict the runtime environment online, but also has strong scalability and continuous learning ability. It is worth noting that our method exhibits excellent online prediction performance for runtime environments in large-scale colocation scenarios.

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Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Fan Liu, Weilin Zhu, Weimin Mu, Yun Zhang, Mingyang Li, Can Ma & Weiping Wang

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Fan Liu & Weilin Zhu

Authors
  1. Fan Liu
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  2. Weilin Zhu
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  3. Weimin Mu
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  4. Yun Zhang
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  5. Mingyang Li
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  6. Can Ma
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  7. Weiping Wang
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Corresponding author

Correspondence to Weimin Mu .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Liu, F. et al. (2023). Online Runtime Environment Prediction for Complex Colocation Interference in Distributed Streaming Processing. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-36021-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36020-6

  • Online ISBN: 978-3-031-36021-3

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