Autonomic Microservice Management via Agentic AI and MAPE-K Integration
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909662567202816 |
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| author | Esposito, Matteo Bakhtin, Alexander Ahmad, Noman Robredo, Mikel Su, Ruoyu Lenarduzzi, Valentina Taibi, Davide |
| author_facet | Esposito, Matteo Bakhtin, Alexander Ahmad, Noman Robredo, Mikel Su, Ruoyu Lenarduzzi, Valentina Taibi, Davide |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomic Microservice Management via Agentic AI and MAPE-K Integration Esposito, Matteo Bakhtin, Alexander Ahmad, Noman Robredo, Mikel Su, Ruoyu Lenarduzzi, Valentina Taibi, Davide Software Engineering Artificial Intelligence Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Systems and Control 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. |
| title | Autonomic Microservice Management via Agentic AI and MAPE-K Integration |
| topic | Software Engineering Artificial Intelligence Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2506.22185 |