Electricity Consumption and Cost Prediction for Cloud Computing

Author(s)	Ms. Tejaswini Kolliboni, Ms. Neha Balannagari, Ms. Ramya Sri Daram, Ms. Geethika Indla
Country	India
Abstract	Cloud computing's rapid ascent in the IT industry simplifies tasks by eliminating the need for physical hardware, with services managed by cloud providers operating electricity-powered computers. The design and upkeep of these facilities depend on affordable, consistent electrical power. However, cloud centers face challenges in reducing energy consumption, highlighted by recent spikes in electricity expenses. To address this, optimizing data placement and node scheduling is crucial. This article introduces an approach using Random Forest and XGBoost models to facilitate storage offloading, predict electricity pricing trends, and reduce energy expenditure in data centers. The proposed work aims to enhance energy awareness, enabling other management tools to make informed, energy-efficient decisions and reduce electricity consumption, lowering costs for cloud providers and minimizing environmental impact.
Keywords	Machine Learning, Random Forest, Extreme Gradient Boosting, Regression Model, Root Mean Squared Error, Mean Absolute Error
Field	Computer > Data / Information
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-29
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6660
Short DOI	https://doi.org/g9r8d7

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