Artificial Intelligence-Driven Predictive Analytics for Cloud Capacity Planning

• Author(s): Jeyasri Sekar
• Paper ID: 1704935
• Page: 667-674
• Published Date: 31-08-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 2 August-2023

Abstract

The objective of this research is to develop and evaluate an artificial intelligence (AI)-driven predictive analytics model for cloud capacity planning. This involves leveraging machine learning algorithms to forecast resource demand and optimize cloud infrastructure utilization. The study aims to address the inefficiencies and limitations of traditional capacity planning methods, providing a more accurate and scalable solution for cloud service providers. The research focuses on improving prediction accuracy, reducing operational costs, and enhancing overall cloud performance. To achieve the research objectives, a variety of AI techniques were employed, including data preprocessing, collection, and cleaning of historical cloud usage data to ensure data quality and relevance. Feature engineering was used to identify and extract key features that influence cloud resource usage. Various machine learning algorithms such as linear regression, decision trees, random forests, and neural networks were evaluated to identify the most effective model for predictive analytics. The selected model was trained using a significant portion of the historical data, with hyper parameter tuning to optimize model performance. Validation and testing were conducted using cross-validation techniques and a separate test dataset to assess the accuracy and robustness of the model predictions. The trained model was then integrated into a cloud capacity planning framework to automate resource allocation and scaling decisions. The AI-driven predictive analytics model demonstrated significant improvements over traditional capacity planning methods. It achieved a higher accuracy rate in forecasting cloud resource demands, reducing the margin of error in capacity planning. This enabled more precise allocation of resources, leading to substantial cost savings by minimizing over-provisioning and under-provisioning of cloud infrastructure. The model proved to be scalable, handling large volumes of data and adapting to varying cloud environments without compromising performance. Key performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) showed marked improvement, validating the effectiveness of the AI model. The findings of this research underscore the potential of AI-driven predictive analytics in revolutionizing cloud capacity planning. By accurately forecasting resource demands and optimizing utilization, cloud service providers can achieve enhanced operational efficiency and cost-effectiveness. The study provides a robust framework for implementing AI techniques in cloud management, highlighting the practical implications and benefits of transitioning from traditional methods to advanced predictive analytics. Future research could explore the integration of real-time data and adaptive learning algorithms to further refine and enhance the predictive capabilities of the model.

Keywords

Predictive Analytics, Cloud Capacity Planning, Artificial Intelligence, Machine Learning, Resource Management

Citations

IRE Journals:
Jeyasri Sekar "Artificial Intelligence-Driven Predictive Analytics for Cloud Capacity Planning" Iconic Research And Engineering Journals Volume 7 Issue 2 2023 Page 667-674

IEEE:
Jeyasri Sekar "Artificial Intelligence-Driven Predictive Analytics for Cloud Capacity Planning" Iconic Research And Engineering Journals, 7(2)