Prototype of 3D Reliability Assessment Tool Based on Deep Learning for Edge OSS Computing
Yoshinobu Tamura, Shigeru Yamada · Mathematics · 2022
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.