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International Conference on Services Computing

SCC 2020: Services Computing – SCC 2020 pp 137–144Cite as

Midiag: A Sequential Trace-Based Fault Diagnosis Framework for Microservices

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12409))

Abstract

Cloud applications are often deployed in shared data centers to optimize resource allocation and improve management efficiency. However, since a cloud application often has a large amount of different microservices, it is difficult for operators to analyze these microservices with a unified model. To deal with the above problem, this paper proposes a sequential trace-based fault diagnosis framework called as Midiag by mining the patterns of microservices’ system call sequences. Midiag collects system calls with a non-invasive lightweight tool, and then uses k-means to cluster system call sequences as patterns with the longest common subsequence. The GRU-based neural network is employed to model the patterns of system call sequences to predict the next system call, and thus we can diagnose faults by comparing the predicted next system call and the actual next one in the specific pattern. We have validated Midiag with many different types of applications deployed in containers. The results demonstrate that Midiag can well classify these applications as different types and accurately diagnose the injected faults.

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Acknowledgment

This work is supported by National Key R&D Program of China (2018YFB1402900).

Author information

Authors and Affiliations

  1. College of Public Administration, Hohai University, Nanjing, 210098, China

    Lun Meng

  2. Nanjing Institute of Big Date, Jinling Institute of Technology, Nanjing, 211169, China

    Yao Sun

  3. Information Engineering College, Capital Normal University, Beijing, 100048, China

    Shudong Zhang

Authors
  1. Lun Meng
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  2. Yao Sun
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  3. Shudong Zhang
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Corresponding author

Correspondence to Shudong Zhang .

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Editors and Affiliations

  1. Louisana State University, Baton Rouge, LA, USA

    Qingyang Wang

  2. Chongqing University, Chongqing, China

    Yunni Xia

  3. IBM Almaden Research Center, San Jose, CA, USA

    Sangeetha Seshadri

  4. Kingdee International Software Group Co. Ltd., Shenzhen, China

    Liang-Jie Zhang

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Meng, L., Sun, Y., Zhang, S. (2020). Midiag: A Sequential Trace-Based Fault Diagnosis Framework for Microservices. In: Wang, Q., Xia, Y., Seshadri, S., Zhang, LJ. (eds) Services Computing – SCC 2020. SCC 2020. Lecture Notes in Computer Science(), vol 12409. Springer, Cham. https://doi.org/10.1007/978-3-030-59592-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-59592-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59591-3

  • Online ISBN: 978-3-030-59592-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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