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

Mathematics

Shigeru Yamada¹  Yoshinobu Tamura² 

[1] Graduate School of Engineering, Tottori University, Tottori 680-8552, Japan;Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 755-8611, Japan;

关键词: fault big data;    software tool;    visualization;    fault severity level;    fault correction time;    deep learning;   

DOI  :  10.3390/math10091572

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown