Self-Healing AI: Leveraging Cloud Computing for Autonomous Software Recovery
Keywords:
Self-healing AI, cloud computing, autonomous recovery, fault diagnosis, machine learning, system resilience.Abstract
As software systems grow increasingly complex and integrated, ensuring resilience against unexpected failures becomes a paramount concern. Self-healing Artificial Intelligence (AI) offers a transformative solution by enabling software systems to autonomously detect, diagnose, and recover from faults. This paper explores the integration of self-healing AI with cloud computing technologies to enhance software recovery capabilities. By leveraging the scalability and computational power of cloud platforms, self-healing AI systems can implement real-time monitoring, predictive analytics, and fault remediation across distributed environments. The proposed framework employs machine learning algorithms to predict potential failures by analyzing historical performance data and real-time metrics. Reinforcement learning models are used to optimize recovery actions, balancing system stability and operational efficiency. The elasticity of cloud computing resources allows self-healing AI to dynamically allocate computational power for fault diagnosis and resolution without compromising performance. Furthermore, this paper discusses the role of microservices architectures and containerization in enabling granular self-healing capabilities, ensuring minimal disruption during recovery. The study presents experimental results demonstrating the efficacy of cloud-integrated self-healing AI in reducing downtime and enhancing system reliability. The framework achieved up to a 92% reduction in mean time to recovery (MTTR) compared to traditional reactive approaches. Key challenges, such as data security, latency, and resource overhead, are also addressed, emphasizing the importance of robust architectural design and data encryption techniques.
This research contributes to the growing body of knowledge on autonomous software recovery by combining the adaptive learning capabilities of AI with the scalability of cloud computing. It provides a pathway for organizations to build resilient software systems capable of withstanding the demands of dynamic and unpredictable operational environments.
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