Self-Healing Microservices and AI-Based Anomaly Detection System

The rapid growth of distributed microservice architectures has introduced significant challenges in ensuring reliability, scalability, and fault tolerance.This paper proposes a Self-Healing Microservices and AI-Based Anomaly Detection System designed to automatically detect, diagnose, and recover from failures in real time.The system integrates artificial intelligence models for anomaly detection with automated orchestration tools to continuously monitor health and performance metrics of microservices.Upon identifying anomalies, the system autonomously initiates corrective actions such as fault isolation and resource reallocation to maintain service continuity.The proposed framework employs predictive analytics to forecast potential failures, enabling proactive recovery and minimizing downtime.Experimental evaluation demonstrates that the system achieves notable improvements in recovery speed, reduced failure impact, and enhanced overall resilience compared to traditional reactive systems.The study concludes that integrating AI with self-healing mechanisms in cloud-native environments can significantly improve system reliability and availability under dynamic workloads, contributing to the development of intelligent, autonomous, and robust microservice ecosystems capable of sustaining optimal performance in complex distributed settings.