Using An Improved Machine Learning Model, Electricity Price Forecasting for Cloud Computing

M. Jabeer Khan and R. Viswanathan

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 206-210

Abstract: The rapid expansion and adoption of cloud computing have revolutionized the way organizations manage and deliver services, enabling greater scalability, flexibility, and cost-effectiveness. However, the efficient operation of cloud data centers relies heavily on accurate electricity price forecasting, as electricity costs constitute a substantial portion of the operational expenses. In this context, this research proposes a pioneering approach that leverages an improved machine learning model to predict electricity prices for cloud computing, enabling better resource allocation and cost optimization. Cloud computing has emerged as a dominant paradigm for delivering a wide range of services, including infrastructure, platform, and software solutions, over the internet. As more businesses and industries embrace cloud services, the demand for efficient resource management and cost optimization in cloud data centers becomes increasingly paramount. Electricity, as a primary operational cost, significantly impacts the overall expenses incurred by cloud service providers. Hence, accurate and reliable electricity price forecasting is critical to ensuring sustainable and cost-effective cloud operations.

Keywords: Data Storage; Energy Saving; Electricity Price Forecasting; XG Boost (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390281
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