AI-powered fault detection and mitigation in cloud computing infrastructures

In the evolving landscape of cloud computing, ensuring the reliability and resilience of cloud infrastructures has become paramount. This study investigates the application of artificial intelligence (AI) for fault detection and mitigation in cloud computing environments. Traditional fault detection methods often struggle to cope with the dynamic and complex nature of modern cloud infrastructures, leading to suboptimal performance and increased downtime. Our research leverages advanced AI algorithms to identify and mitigate faults in real-time, thereby enhancing system reliability and performance.
The primary objectives of this research are to develop and validate AI models that can accurately detect faults in cloud computing infrastructures, evaluate the effectiveness of these models in mitigating detected faults, and compare their performance against traditional fault detection and mitigation techniques. We aim to demonstrate that AI-powered solutions can significantly reduce the incidence and impact of faults in cloud systems.
Key findings of the study reveal that AI-powered fault detection models exhibit superior accuracy and speed compared to conventional methods. The implementation of these models resulted in a marked improvement in fault mitigation, reducing system downtime and enhancing overall service quality. Additionally, the study identifies specific AI techniques, such as machine learning and deep learning, that are particularly effective in this context.