CLOUDPROPHET: AI-based performance prediction for public cloud environments

With the growing dependence on cloud infrastructure for compute-heavy applications, optimizing cloud resource usage is more crucial than ever. CloudProphet presents a novel method that leverages machine learning to forecast virtual machine (VM) performance in public cloud setups. This system employs Dynamic Time Warping (DTW) for identifying application patterns and Pearson correlation to isolate the most significant runtime metrics. These refined features are then processed through three model configurations: a base LSTM, an LSTM enhanced with DTW and selected metrics, and a GRU model that integrates both enhancements. Among these, the GRU model delivers the highest performance, attaining an impressive 99.3% accuracy. Using both GitHub datasets and live testing scenarios, CloudProphet effectively predicts VM behaviors and application categories, demonstrating its potential to boost cloud resource efficiency in real-world settings.