Optimizing Cloud Resources By Anomaly Detection And Machine Learning For Smarter Power Consumption And Execution Time Predictions

The development of cloud computing has made it necessary that the resources should be managed efficiently so as to improve performance as well as sustain the system for the long term. This research aims to explore the application of anomaly detection techniques to monitor and evaluate various time-critical metrics, including but not limited to: CPU load, memory size, network utilization, power consumption, time taken to execute actions, and energy effectiveness within cloud environments. Employing machine learning methods, including Random Forest regression models build forecasting models for energy consumption and duration of operations based on past events and extensive feature development. In addition to these developments, certain statistical techniques, including rolling averages and appropriate percentiles, assist in increasing the strength of predictions. Model evaluation is conducted via metrics such as MSE and R2 to confirm the neat accomplishment of the techniques in predicting anomalies and the behaviors of the resources. Also, a real-time alerting system is suggested to inform the admin about the instance of power and time consumption exceeding the set critical values. The research demonstrates how the deployment of anomaly detection and machine learning techniques can improve the current cloud computing operations, leading to a greener and more effective.