An Optimized Machine Learning Approach for Electricity Price Prediction in Cloud Data Centers

Cloud computing has revolutionized the IT sector as physical infrastructure dependence is minimized, but the energyconsuming nature of the large-scale data center has led to electricity being a critical issue. The recent dynamism in electricity prices makes the effective management of resources in cloud environment more difficult. In a bid to resolve this problem, this paper suggests an improved machine learning model on the problem of electricity price prediction and efficient allocation of resources using Extreme Gradient Boosting (XGBoost). The model is also created to enhance the location of data and scheduling of nodes that decreases the use of energy and operational costs. An actual dataset used to evaluate it is of the Independent Electricity System Operator (IESO), Ontario, Canada, where data are divided into 70 percent of training and 30 percent of testing. The experimental findings prove that the suggested method provides correct prediction of electricity prices and allows cloud data centers to schedule their activities energy-consciously. This makes cloud computing infrastructures more sustainable, cost-efficient and environmentally friendly.