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DashChef: A Metric Recommendation Service for Online Systems Using Graph Learning

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Engineering of Complex Computer Systems (ICECCS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14784 ))

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Abstract

To ensure the high availability of modern online systems, effective maintenance is of critical importance. Today’s software maintenance techniques for online systems heavily rely on metrics, which are time series data that can describe the real-time state of a system from various perspectives. Typically, software engineers generate dashboards with metrics to aid software maintenance. Though several attempts have been devoted to metric analysis for automatic software maintenance, the primary step, i.e., dashboard generation, remains manual to a large extent. In this paper, we develop a metric recommendation service, which can automate the dashboard generation practice and greatly ease the burden in maintaining an online system. Specifically, we analyze the needs of two essential steps of online system maintenance, i.e., anomaly detection and fault diagnosis, and design metric recommendation mechanisms for them respectively. Graph learning techniques are employed in the automation of metric recommendation. Our experiments demonstrate that the proposed approach can achieve an F1-score of 0.912 in selecting metrics for anomaly detection, and an accuracy of 0.859 in retrieving metrics for faults diagnosis, which significantly outperforms the compared baselines.

Z. He and T. Huang—Co-first authors of this work.

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Acknowledgments

The research is supported by the National Natural Science Foundation of China (No. 62272495) and the Guangdong Basic and Applied Basic Research Foundation (No. 2023B1515020054), and sponsored by Tencent.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Zilong He & Pengfei Chen

  2. Tencent, Shenzhen, China

    Tao Huang, Ruipeng Li & Rui Wang

  3. School of Software Engineering, Sun Yat-sen University, Guangzhou, China

    Zibin Zheng

Authors
  1. Zilong He
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  2. Tao Huang
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  3. Pengfei Chen
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  4. Ruipeng Li
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  5. Rui Wang
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  6. Zibin Zheng
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Corresponding author

Correspondence to Pengfei Chen .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia

    Guangdong Bai

  2. Information Systems Architecture Science Research Division, National Institute of Informatics, Chiyoda-ku, Tokyo, Japan

    Fuyuki Ishikawa

  3. IRIT, University of Toulouse, Toulouse, France

    Yamine Ait-Ameur

  4. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

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He, Z., Huang, T., Chen, P., Li, R., Wang, R., Zheng, Z. (2025). DashChef: A Metric Recommendation Service for Online Systems Using Graph Learning. In: Bai, G., Ishikawa, F., Ait-Ameur, Y., Papadopoulos, G.A. (eds) Engineering of Complex Computer Systems. ICECCS 2024. Lecture Notes in Computer Science, vol 14784 . Springer, Cham. https://doi.org/10.1007/978-3-031-66456-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-66456-4_1

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