AI-Enhanced Monitoring and Alerting in DevOps for Proactive Issue Resolution - Charan, Kolli; Narasimharao, Pujala Bhogeswara; Abhilash, Vaka; Karthikeya, Nadipineni; Devi, S. Anjali (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In today’s fast-paced and dynamic software development landscape, DevOps practices have become instrumental in accelerating software delivery while maintaining reliability. Central to this methodology is the monitoring and alerting system, which plays a critical role in detecting and addressing issues promptly. This paper explores the integration of Artificial Intelligence (AI) into the DevOps ecosystem to enhance monitoring and alerting capabilities, thereby enabling proactive issue resolution. The primary objective of this study is to elucidate the ways in which AI technologies, such as machine learning and data analytics, can be harnessed to improve the efficiency and effectiveness of DevOps monitoring and alerting. It highlights the challenges faced by traditional monitoring systems, including high false positive rates and delayed issue detection, and discusses how AI can mitigate these challenges. Furthermore, the paper delves into the practical implementation of AI-enhanced monitoring and alerting within DevOps, emphasizing the role of anomaly detection, predictive analytics, and intelligent alerting mechanisms. It also explores the potential benefits of AI in terms of reducing downtime, optimizing resource allocation, and ultimately enhancing the user experience. The study concludes by summarizing the advantages and potential pitfalls of integrating AI into DevOps monitoring and alerting, while also addressing the ethical and security considerations that need to be taken into account. The findings of this research are expected to be of interest to DevOps practitioners, software engineers, and organizations aiming to improve their operational efficiency and maintain a competitive edge in the digital era.