Advanced Predictive Analytics Framework for Intelligent Resource Optimization in Cloud Infrastructure Systems
Abstract
The rapid growth of cloud computing has created increasingly dynamic infrastructure environments in which resource demand can fluctuate significantly across applications, services, workloads, and geographical locations. Conventional cloud resource management approaches often rely on static provisioning, threshold-based scaling, or reactive monitoring, which can result in resource underutilization, unnecessary infrastructure costs, performance degradation, and service-level violations. This research proposes an advanced predictive analytics framework for intelligent resource optimization in cloud infrastructure systems. The proposed framework integrates cloud telemetry, machine learning, time-series forecasting, anomaly detection, workload characterization, resource-demand prediction, and automated optimization into a unified architecture. Historical and real-time data relating to CPU utilization, memory consumption, storage activity, network traffic, application response time, workload intensity, and service-level indicators are continuously collected and processed. Predictive models estimate future resource requirements, while optimization mechanisms determine appropriate resource allocation and scaling strategies. The framework incorporates adaptive learning to address changing workload patterns and model drift. Cloud-native microservices and container orchestration enable the predictive analytics components to scale according to operational requirements. A multi-objective optimization strategy considers performance, cost, resource utilization, energy efficiency, and service-level objectives when generating resource-allocation recommendations. Explainable AI mechanisms provide insight into the factors influencing predictions, while continuous monitoring evaluates model performance and infrastructure behavior. The methodology evaluates forecasting accuracy, resource utilization, cost reduction, response latency, scalability, and service reliability. The proposed framework aims to enable proactive rather than reactive cloud resource management, improving infrastructure efficiency, reducing operational costs, and maintaining reliable enterprise application performance.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 9 No. 5 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
16454-16464 |
Published |
October 9, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mark Richards (%2024). Advanced Predictive Analytics Framework for Intelligent Resource Optimization in Cloud Infrastructure Systems. International Journal of Emerging Trends in Engineering and Management Research , Vol. 9 No. 5 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 16454-16464. https://doi.org/10.15662/ijetemr.2024.0905004 |
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