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

Yoshinobu Tamura and Shigeru Yamada
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Yoshinobu Tamura: Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 755-8611, Japan
Shigeru Yamada: Graduate School of Engineering, Tottori University, Tottori 680-8552, Japan

Mathematics, 2022, vol. 10, issue 9, 1-20

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.

Keywords: fault big data; software tool; visualization; fault severity level; fault correction time; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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