Cloud-Based Resource Management Optimization Model based on Twin-Dynamic Programming Algorithms: A Simulation-Based Approach

Abstract

Cloud computing has emerged as a paradigm for delivering scalable and on demand computing resources over the internet. However, efficient resource management remains a significant challenge in cloud computing environments. This study proposes a novel cloud-based resource management optimization model based on twin-dynamic programming algorithm, which combines the strengths of dynamic programming and simulation based optimization. The proposed model aims to optimize resource allocation and minimize cost in cloud computing environments. We implement the proposed model using the CloudSim simulator evaluate its performance under various workload scenarios. The simulation results demonstrate that the model outperforms existing traditional resource management approaches in terms of resource utilization, execution time, throughput, makespan, cost savings and quality of service (QOS). The proposed model provides a promising solution for cloud providers to optimize resource management and improve the efficiency of cloud computing environments. Future studies can explore the proposed model in real world computing environments and investigate the impact of different workload patterns and system configuration on the performance of the proposed model.