AI-Driven Incident Management in Microservices: A Scalable and Cost-Effective Framework for Proactive Site Reliability
DOI:
https://doi.org/10.63397/ISCSITR-IJCSE_2025_06_04_002Keywords:
Microservices, AI, Cost, ScalabilityAbstract
The current paper develops a scalable AI-backed framework to manage microservices-based architecture, and incident management that would help to promote proactive site reliability. By incorporating anomaly detection, smart correlations of alerts and autonomous measures, the system overwhelms Mean Time to Resolution (MTTR) as well as the recurrence of incidents. According to the deployment results in real life, a better detection rate of anomalies, reduced overheads in operations, and availability of services were noted. The framework makes use of multi-source telemetry, reinforcement learning, bots, and explainable AI models in decision support. It is able to handle hybrid and multi-cloud environments in a faultless way, which proposes an engaging experience in terms of being cost effective and the smart way to self-healing of systems in current enterprise IT systems.
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Copyright (c) 2025 Sunil Agarwal (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
