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Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing

Author

Listed:
  • Yoshinobu Tamura

    (Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 755-8611, Japan
    These authors contributed equally to this work.)

  • Shigeru Yamada

    (Graduate School of Engineering, Tottori University, Tottori 680-8552, Japan
    These authors contributed equally to this work.)

Abstract

We focus on an estimation method based on deep learning in terms of fault correction time for the operation reliability assessment of open-source software (OSS) under the environment of an edge computing service. Then, we discuss fault severity levels in order to consider the difficulty of fault correction. We use a deep feedforward neural network in order to estimate fault correction times. In particular, we consider the characteristics of fault trends by using three-dimensional graphs. Therefore, we can increase the recognizability of the proposed method based on deep learning for large-scale fault data from the standpoint of fault severity levels under edge OSS operation.

Suggested Citation

  • Yoshinobu Tamura & Shigeru Yamada, 2022. "Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1572-:d:809957
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    References listed on IDEAS

    as
    1. Ji, Chenyi & Su, Xing & Qin, Zhongfu & Nawaz, Ahsan, 2022. "Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Shigeru Yamada & Yoshinobu Tamura, 2016. "OSS Reliability Measurement and Assessment," Springer Series in Reliability Engineering, Springer, edition 1, number 978-3-319-31818-9, December.
    3. P.K. Kapur & Hoang Pham & A. Gupta & P.C. Jha, 2011. "Software Reliability Assessment with OR Applications," Springer Series in Reliability Engineering, Springer, number 978-0-85729-204-9, December.
    Full references (including those not matched with items on IDEAS)

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