An Intelligent DevOps Platform Research and Design Based on Machine Learning

被引：2
|
作者：
Wang, Zeqi [1 ]
Shi, Minyong [1 ]
Li, Chunfang [1 ]
机构：
[1] Commun Univ China, Sch Comp Sci & Cybersecur, Beijing, Peoples R China
来源：
2020 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED CLOUD AND BIG DATA (CBD 2020) | 2020年
关键词：
DevOps; machine learning; platform design;
D O I：
10.1109/CBD51900.2020.00017
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the continuous deepens and expansion of IT business based on AI, machine learning and blockchain technologies, there are many developments in intelligent communication and Internet industries. Matured IT business cause daily DevOps (Development & Operations) works must deal with huge amounts of data. What the trickier work gradually emerged is that these data have complex sources, various formats, and other issues. Efficient and inexpensive DevOps of computer software and hardware systems become an important task which needs to be resolved. In SLC (Software Life Cycle), DevOps occupies more than half proportion. It impact entire IT business reflected in the business overall control, business risk control, and business cost control. In order to improve the efficiency of DevOps engineers and ensure the high-quality intelligence level of DevOps, this project starts with the DevOps theoretical framework, use machine learning method to do research, and design an intelligent DevOps platform, which can help engineers analyze huge amounts of multifarious system alarms, promotes the development of DevOps in the direction of informatization.
引用
收藏
页码：42 / 47
页数：6
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