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The research aims to explore how AI enhances autonomic control for self-healing in distributed systems.

May 8, 2026Open Access

AI-Driven Autonomic Control With Machine Learning For Self-Healing Distributed Systems

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This overview synthesizes AI techniques for enhancing self-healing in distributed systems, suggesting promising directions for future development.

The research aims to explore how AI enhances autonomic control for self-healing in distributed systems.
Synthesis of autonomic computing architectures and control models.
Analysis of reinforcement learning, probabilistic reasoning, and hybrid AI techniques.
Evaluation of existing literature to identify empirical studies and research challenges.
AI techniques significantly improve fault detection and recovery planning in distributed systems.
Learning-based decision systems can adapt to various failure scenarios effectively.
Key studies reveal ongoing challenges that motivate advances in intelligent self-healing mechanisms.

Thompson et al. (2021) studied this question.

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This overview synthesizes AI techniques for enhancing self-healing in distributed systems, suggesting promising directions for future development.