Machine Learning Assisted API Management for Self Healing Enterprise Systems and Cloud Native Operations
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Abstract
Modern enterprise systems increasingly rely on Application Programming Interfaces (APIs) to enable communication among distributed applications, cloud services, microservices, and third-party platforms. As organizations adopt cloud-native architectures, maintaining API availability, performance, and security becomes a critical challenge due to dynamic workloads, infrastructure failures, configuration errors, and cyber threats. Machine Learning (ML) has emerged as a transformative technology for API management by enabling predictive analytics, intelligent monitoring, anomaly detection, automated fault diagnosis, and self-healing capabilities. Self-healing enterprise systems leverage ML models to identify abnormal behavior, predict failures before they occur, recommend corrective actions, and automatically execute recovery processes with minimal human intervention. These capabilities significantly improve system reliability, reduce downtime, enhance operational efficiency, and optimize resource utilization in cloud environments. Furthermore, ML-assisted API management supports adaptive traffic routing, intelligent load balancing, predictive scaling, and continuous security monitoring, making cloud-native operations more resilient and autonomous. This study explores the integration of machine learning techniques into API management frameworks to develop intelligent self-healing enterprise systems. It reviews existing literature, discusses key technologies, proposes an appropriate research methodology, and highlights implementation challenges and future opportunities. The study concludes that combining machine learning with modern API management platforms represents a significant advancement toward autonomous cloud operations and resilient digital enterprise ecosystems
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