<![CDATA[Efficient resource allocation remains a critical challenge in cloud computing environments due to the dynamic and heterogeneous nature of workloads and infrastructure. This paper presents a comprehensive modelling perspective to address the complexities of resource management, aiming to optimize performance while minimizing operational costs. We propose a flexible and scalable modelling framework that integrates workload characterization, predictive demand analysis, and optimization algorithms to support decision-making in resource allocation. The framework is validated through extensive simulations using real-world workload traces and benchmark scenarios. Results demonstrate significant improvements in resource utilization, energy efficiency, and service-level agreement (SLA) compliance compared to existing approaches. This study highlights the importance of model-driven strategies in enhancing the adaptability and efficiency of cloud resource management systems.]]>
Copyrights © 2023
