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

Optimizing resource consumption in cloud infrastructure, the key monitoring, detection and prediction of faults are most relevant task. Optimizing resource usage in the cloud infrastructure is quite challenging. We propose a framework that utilizes Prediction and Monitoring techniques to detect fault and optimize the resource allocation and reduces the downtimes. By employing machine learning algorithms to scrutinise performance data from the past, the framework is able to anticipate fault events before they happen. Besides, this prediction ability takes action at the right moment to improve the operational reliability. A recursive is also built-in, allowing the model to improve performance continuously, based on real-time insights from cloud operations. From the experiments conducted in different cloud environments, considerable enhancements have been obtained in fault detection rates and resource utilization efficiency as compared to those in existing approaches thus highlighting the importance of predictive analytics in building robust open cloud infrastructure.