Predictive Analytics for Cloud Resource Planning and Cost Forecasting

Predictive Analytics for Cloud Resource Planning and Cost Forecasting | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 May 2025 V1 Latest version Share on Predictive Analytics for Cloud Resource Planning and Cost Forecasting Author : Edward Kass 0009-0001-4921-8946 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174768394.48353029/v1 375 views 184 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract As organizations increasingly rely on cloud infrastructure to drive digital transformation, the need for intelligent, data-driven cloud resource planning and cost management has become more critical than ever. Predictive analytics-a discipline that leverages statistical algorithms, machine learning models, and historical data-has emerged as a powerful enabler for enhancing decision-making in cloud environments. This paper explores the application of predictive analytics in cloud resource planning and cost forecasting, highlighting its potential to transform reactive resource management into a proactive and strategic function. The abstract introduces key predictive techniques such as time series analysis, regression models, and neural networks, which are used to forecast workload demands, storage utilization, and budgetary trends. It discusses how these models can analyze vast volumes of historical usage and billing data to predict future cloud consumption with high accuracy. Through these predictions, organizations can automate capacity planning, optimize infrastructure investments, and reduce the risk of over-provisioning or underutilization. Furthermore, predictive cost forecasting helps finance and DevOps teams collaborate effectively by providing granular visibility into expected expenses, enabling better budgeting and reducing financial surprises. The abstract also addresses challenges in implementing predictive analytics, such as data quality issues, model drift, and the complexity of multi-cloud environments. Additionally, it reviews emerging trends, including the integration of AI-driven anomaly detection, real-time predictive dashboards, and FinOps alignment strategies. By enabling smarter cloud governance, predictive analytics empowers businesses to achieve cost efficiency, scalability, and performance optimization. This paper concludes that predictive analytics is not merely a tactical tool for cloud management, but a strategic asset that can deliver competitive advantage in an era of agile, cloud-first enterprises. Supplementary Material File (predictive analytics for cloud resource planning and cost forecasting.pdf) Download 699.14 KB Information & Authors Information Version history V1 Version 1 19 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keyword storage utilization Authors Affiliations Edward Kass 0009-0001-4921-8946 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 375 views 184 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Edward Kass. Predictive Analytics for Cloud Resource Planning and Cost Forecasting. Authorea . 19 May 2025. DOI: https://doi.org/10.22541/au.174768394.48353029/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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