An Intelligent DevOps Platform Research and Design Based on Machine Learning
Zeqi Wang, Minyong Shi, Chunfang Li
Abstract
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.