Intelligent Fault Analysis with AIOps Technology

Authors

  • Yuhang He Computer Science and Technology ,Tianjin University of Technology,Tianjin ,China
  • Yiming Pan Computer Science, Colorado Technical University, Colorado Spring, CO,USA
  • Yong Wang Information Technology, University of Aberdeen, Aberdeen, United Kingdom
  • Shuqian Du Information Studies, Trine University, Phoenix, Arizona, AZ,USA
  • Qi Xin Management Information Systems, University of Pittsburgh, Pittsburgh, PA, USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(01).13

Keywords:

AIOps, Fault analysis, Intelligent operation and maintenance

Abstract

With the deep integration of artificial intelligence (AI) and operation and maintenance management, AIOPS (Artificial Intelligence operation and maintenance), as an emerging technology in the field of operation and maintenance management, has gradually attracted wide attention. AIOPS is committed to improving the efficiency and quality of traditional operations management through the introduction of automation and intelligent technology, and has become a hot topic in the industry. The development of AIOPS is inseparable from the identification of patterns and anomalies in data, which requires the application of machine learning, deep learning and other technologies. The rise of AIOPS is also due to the development of big data, cloud computing and container technology. These new technologies make traditional operation and maintenance monitoring means and processing methods powerless. Traditional troubleshooting methods can no longer meet the needs of large-scale and complex system monitoring and problem positioning. Therefore, this paper combines the implementation process and case analysis of AIOps system on intelligent fault analysis under the background of artificial intelligence, so as to analyze the development prospects and improvement suggestions of AIOps.

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Published

2024-02-01

How to Cite

He, Y., Pan, Y., Wang, Y., Du, S., & Xin, Q. (2024). Intelligent Fault Analysis with AIOps Technology. Journal of Theory and Practice of Engineering Science, 4(01), 94–100. https://doi.org/10.53469/jtpes.2024.04(01).13

Issue

Vol. 4 No. 01 (2024): JTPES-2023-4-1

Section

Articles

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Copyright (c) 2024 Yuhang He, Yiming Pan, Yong Wang, Shuqian Du, Qi Xin

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.