PRIMA: An Intelligent Prediction-Driven Framework for Proactive Cloud Resource Management

Traditional cloud resource management software typically perform workload prediction, VM allocation and migration policies independently, yielding sub-optimal resource utilization and excessive migration overhead. This study presents PRIMA, an optimization-driven prediction-assisted resource management framework for efficient resource management in cloud environment. The framework leverages neural model to forecast memory and CPU utilization of VMs, wherein Raindrop Optimization Algorithm is employed to enhance prediction accuracy. Based on the predicted workload, VMs are proactively mapped to physical servers to optimize resource utilization and minimize server overload. Additionally, a multi-objective VM migration strategy consolidates workloads by minimizing active servers without compromising service quality. Experimental evaluation using real-world Bitbrains workload traces along with additional validation using the Google Cluster Dataset demonstrates that PRIMA attains consistent improvements in resource management performance, yielding up to 9.63% reduction in power consumption, 75% reduction in conflicting servers and 73.24% reduction in VM migrations, and a 13.46% enhancement in resource utilization in comparison to RNN-LSTM, SVM, ARIMA, and HHO-based prediction-assisted baseline frameworks. Overall, PRIMA provides a unified and proactive alternative to conventional prediction-assisted cloud resource management approaches, making it suitable for intelligent cloud computing systems.