Predictive Capacity Planning for SAP Cloud Platforms Through Workload Modeling and Adaptive Resource Optimization
Abstract
Predictive capacity planning is essential for maintaining performance, scalability, and cost efficiency across SAP cloud platforms that support complex enterprise workloads. Traditional capacity management approaches often rely on historical utilization measurements and reactive resource allocation, which may lead to overprovisioning, service degradation, and inefficient infrastructure expenditure during changing workload conditions. This research proposes a predictive capacity planning framework that integrates workload modeling, machine learning-based forecasting, and adaptive resource optimization for SAP cloud environments. The framework analyzes historical resource consumption, transaction volumes, application response times, database activity, seasonal demand patterns, and workload dependencies to predict future infrastructure requirements. Time-series forecasting and regression techniques estimate CPU utilization, memory consumption, storage demand, and network throughput, while an optimization mechanism recommends resource allocation strategies according to performance objectives, budget constraints, and service-level agreements. The proposed methodology incorporates continuous monitoring, forecast validation, and feedback-driven model adaptation to respond to workload fluctuations and evolving enterprise requirements. Evaluation is designed around forecasting accuracy, resource utilization, response latency, operational cost, and scalability under varying workload conditions. The framework aims to reduce unnecessary resource provisioning while preserving application reliability and business continuity. By combining predictive analytics with adaptive resource management, the proposed approach establishes a systematic foundation for intelligent capacity planning across SAP S/4HANA, SAP HANA Cloud, and interconnected enterprise cloud services.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
14773-14781 |
Published |
December 18, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr. Thittaporn Ganokratanaa (%2023). Predictive Capacity Planning for SAP Cloud Platforms Through Workload Modeling and Adaptive Resource Optimization. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14773-14781. https://doi.org/10.15662/ijetemr.2023.0806011 |
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