Autonomic Microservice Management via Agentic AI and MAPE-K Integration
This work addresses the problem of managing highly distributed microservices for cloud computing practitioners, offering an incremental improvement through integration of existing concepts.
The paper tackles the security and management challenges of decentralized microservices by proposing a MAPE-K-based framework with agentic AI for autonomous anomaly detection and remediation, aiming to enhance system stability and reduce downtime.
While microservices are revolutionizing cloud computing by offering unparalleled scalability and independent deployment, their decentralized nature poses significant security and management challenges that can threaten system stability. We propose a framework based on MAPE-K, which leverages agentic AI, for autonomous anomaly detection and remediation to address the daunting task of highly distributed system management. Our framework offers practical, industry-ready solutions for maintaining robust and secure microservices. Practitioners and researchers can customize the framework to enhance system stability, reduce downtime, and monitor broader system quality attributes such as system performance level, resilience, security, and anomaly management, among others.