Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
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 cor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/9/1572 |
| _version_ | 1827672168029224960 |
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| author | Yoshinobu Tamura Shigeru Yamada |
| author_facet | Yoshinobu Tamura Shigeru Yamada |
| author_sort | Yoshinobu Tamura |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:55:50Z |
| format | Article |
| id | doaj.art-b6d73dfc2ebc49a5b52356991acd4c43 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:55:50Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b6d73dfc2ebc49a5b52356991acd4c432023-11-23T08:46:19ZengMDPI AGMathematics2227-73902022-05-01109157210.3390/math10091572Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS ComputingYoshinobu Tamura0Shigeru Yamada1Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 755-8611, JapanGraduate School of Engineering, Tottori University, Tottori 680-8552, JapanWe 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.https://www.mdpi.com/2227-7390/10/9/1572fault big datasoftware toolvisualizationfault severity levelfault correction timedeep learning |
| spellingShingle | Yoshinobu Tamura Shigeru Yamada Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing Mathematics fault big data software tool visualization fault severity level fault correction time deep learning |
| title | Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing |
| title_full | Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing |
| title_fullStr | Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing |
| title_full_unstemmed | Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing |
| title_short | Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing |
| title_sort | prototype of 3d reliability assessment tool based on deep learning for edge oss computing |
| topic | fault big data software tool visualization fault severity level fault correction time deep learning |
| url | https://www.mdpi.com/2227-7390/10/9/1572 |
| work_keys_str_mv | AT yoshinobutamura prototypeof3dreliabilityassessmenttoolbasedondeeplearningforedgeosscomputing AT shigeruyamada prototypeof3dreliabilityassessmenttoolbasedondeeplearningforedgeosscomputing |