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Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
by
Yamada, Shigeru
, Tamura, Yoshinobu
in
Algorithms
/ Artificial neural networks
/ Big Data
/ Cloud computing
/ Datasets
/ Deep learning
/ Edge computing
/ fault big data
/ fault correction time
/ fault severity level
/ Growth models
/ Methods
/ Neural networks
/ Open source software
/ Reliability analysis
/ Software reliability
/ software tool
/ Stochastic models
/ visualization
2022
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Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
by
Yamada, Shigeru
, Tamura, Yoshinobu
in
Algorithms
/ Artificial neural networks
/ Big Data
/ Cloud computing
/ Datasets
/ Deep learning
/ Edge computing
/ fault big data
/ fault correction time
/ fault severity level
/ Growth models
/ Methods
/ Neural networks
/ Open source software
/ Reliability analysis
/ Software reliability
/ software tool
/ Stochastic models
/ visualization
2022
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Do you wish to request the book?
Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
by
Yamada, Shigeru
, Tamura, Yoshinobu
in
Algorithms
/ Artificial neural networks
/ Big Data
/ Cloud computing
/ Datasets
/ Deep learning
/ Edge computing
/ fault big data
/ fault correction time
/ fault severity level
/ Growth models
/ Methods
/ Neural networks
/ Open source software
/ Reliability analysis
/ Software reliability
/ software tool
/ Stochastic models
/ visualization
2022
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Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
Journal Article
Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
2022
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Overview
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.
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