Fault Detection and Prediction in Models: Optimizing Resource Usage in Cloud Infrastructure

Cloud infrastructure management faces significant challenges in ensuring efficient resource usage while maintaining high availability. This paper presents a framework for Fault Detection and Prediction in Models, aiming to optimize resource allocation and minimize downtime by deploying proactive monitoring techniques. The framework leverages machine learning algorithms to analyze historical performance data, enabling the prediction of potential faults before they occur. This predictive capability initiates timely interventions that enhance operational reliability. Additionally, a feedback loop is integrated to continuously improve model performance based on real-time insights from cloud operations. Experiments across various cloud environments demonstrate substantial improvements in fault detection rates and resource utilization efficiency over traditional approaches, underscoring the value of predictive analytics for robust cloud infrastructure management.