Developing a Self-Healing Software Architecture Using AI for Fault Detection and Recovery
Keywords:
AI, AIOps, Autonomous system, Cloud computing, Fault detection,, Intelligent automation, Machine learning, Predictive analytics, Self-healing software, System resilienceAbstract
As software development and deployment continue to advance quickly, maintaining system stability and reducing downtime has become increasingly important. Traditional software systems often require manual intervention to detect, diagnose, and resolve faults, which can lead to delays, increased operational costs, and compromised user experience. To address these limitations, AI-based self-healing software systems are emerging as a transformative solution. Such systems function independently to detect irregularities, forecast potential issues, and carry out recovery steps without requiring manual input by integrating artificial intelligence, machine learning, and observability tools, self-healing systems continuously monitor software behavior and infrastructure metrics in real-time. Leveraging historical data and predictive analytics, the system learns to detect deviations from normal operations and automatically applies remediation techniques such as restarting services, scaling resources, applying patches, or rerouting traffic. This intelligent automation not only enhances system resilience and availability but also reduces the mean time to repair (MTTR), thereby improving overall efficiency and user satisfaction. Furthermore, AI-driven self-healing mechanisms can adapt and evolve with changing system conditions, ensuring scalability and robustness. They are especially beneficial in complex distributed environments like cloud- native applications, microservices architectures, and IoT ecosystems, where manual troubleshooting can be error-prone and time- consuming.
This study examines the structure, key elements, and techniques used in AI-powered self-healing systems, with a focus on their practical uses and advantages. It also addresses key challenges such as false positives, data privacy, and system transparency. The proposed approach aims to move towards autonomous IT operations (AIOps), where software systems become increasingly self-aware and self-reliant, marking a significant step toward the future of intelligent automation in software engineering.
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