Using artificial intelligence and AIOps, automated fault prediction and prevention in Cloud Native settings
Using Artificial Intelligence and AIOps for Automated Fault Prediction and Prevention in Cloud Native Settings
International Journal of Computer Techniques – Volume 11 Issue 6, December 2024 | ISSN: 2394-2231
Author Information
Balajee Asish Brahmandam , Srinath Chandramohan
Summary
Extremely dynamic cloud-native ecosystems, especially those based on microservices architectures, feature intricate interdependencies and constant load fluctuations. This work proposes a fault prediction method driven by artificial intelligence and AIOps to proactively identify and mitigate faults.
Final Thoughts
This paper demonstrates the benefits of AI-powered defect prediction in cloud-native environments. The proposed approach improves system reliability by reducing downtime and enhancing fault detection accuracy.
References
- Chen, L. Y., & Bahsoon, R. (2016). Self-adaptive and self-healing systems. IEEE Transactions on Services Computing.
- Gartner. (2020). Market Guide for AIOps Platforms. Gartner Research.
- Kim, H., & Lee, Y. J. (2021). AIOps architecture and implementation strategies. Journal of Cloud Computing.
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