International Journal on Science and Technology

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AI for Cloud Economics: Predictive Models for Cost-Efficient Resource Allocation

Author(s) Srinivas Chennupati
Country United States
Abstract This article explores the application of artificial intelligence for optimizing cloud economics through predictive models that enable cost-efficient resource allocation. As organizations increasingly adopt cloud technologies, they face significant challenges in managing costs effectively, with many consistently exceeding their cloud budgets due to complex pricing models and the tendency to over-provision resources. Traditional management approaches have proven inadequate in addressing these challenges, creating an opportunity for AI-driven solutions. The article examines four key areas where AI can transform cloud cost management: predictive demand forecasting using time series analysis and machine learning regression models; dynamic resource provisioning and auto-scaling; anomaly detection and workload optimization; and implementation considerations including data privacy, model transparency, and integration challenges. Through a comprehensive analysis of current implementations and emerging technologies, the article demonstrates how AI can enable a shift from reactive to proactive cloud cost management while identifying both opportunities and challenges in adoption. The findings suggest that organizations implementing AI-based optimization strategies can achieve substantial cost reductions while maintaining or improving operational capabilities.
Keywords Cloud Economics, Artificial Intelligence, Resource Optimization, Predictive Forecasting, Cost Efficiency
Field Computer
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-02
Cite This AI for Cloud Economics: Predictive Models for Cost-Efficient Resource Allocation - Srinivas Chennupati - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3074
DOI https://doi.org/10.71097/IJSAT.v16.i2.3074
Short DOI https://doi.org/g9dpf3
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