Autonomous Error Detection and Self-Healing Capabilities in Oracle Fusion Middleware - Reddy Pedda Muntala, Partha Sarathi; Karri, Nagireddy (International Journal of Emerging Research in Engineering and Technology, 2024)
Oracle Fusion Middleware (OFM) forms the backbone of enterprise integration, enabling business process execution through technologies such as Service-Oriented Architecture (SOA), Business Process Execution Language (BPEL), and RESTful APIs. However, as the complexity of enterprise systems escalates, ensuring high availability and resilience becomes a monumental challenge. Autonomous error detection and self-healing mechanisms have emerged as vital solutions to maintain seamless operational workflows in middleware platforms. This paper examines the integration of AI-assisted fault detection in Oracle Fusion Middleware, exploring mechanisms that autonomously identify, diagnose, and remediate faults in SOA composites, BPEL processes, and RESTful service endpoints. We propose a comprehensive self-healing architecture embedded with predictive analytics, anomaly detection algorithms, and dynamic remediation workflows. Leveraging AI technologies, including supervised machine learning and unsupervised clustering, our framework provides proactive monitoring and autonomous recovery. Multiple real-world case studies are analysed to substantiate the feasibility and effectiveness of the proposed solution, with a focus on enterprises such as Infosys, Oracle Corporation, and Accenture. Key contributions include: (1) a layered self-healing architecture, (2) real-time monitoring using AI models, (3) integration of AI agents within Oracle SOA Suite and Oracle Service Bus (OSB), and (4) a prototype evaluated on enterprise workloads. Experimental results demonstrate significant reductions in downtime and maintenance efforts, reinforcing the potential of AI-driven autonomous capabilities in middleware platforms