As cloud computing continues to evolve, optimizing resource utilization and enhancing system efficiency have become preeminent objectives. Efficient VM migration and dynamic virtual machine consolidation strategies stand as pivotal solutions in achieving these goals. However, the success of these approaches hinges on a thorough understanding of the intricate dependencies among virtual machine (VM) components, spanning software, hardware, services, and resources, which are used in deciding competent VM migration strategies. This paper presents an innovative approach focusing on a novel integrated correlation coefficient for conducting dependency analysis of VM components within a virtual cloud infrastructure. Unlike traditional methodologies, our proposed model, efficient migration through dependency analysis (EMDA), not only accounts for resource utilization metrics but also delves into the complex inter-component relationships of VMs that govern computation in cloud environments. By considering various significant factors, our proposed framework offers a comprehensive technique for deciphering VM dependencies. We have developed a novel system architecture with the necessary functional blocks to streamline the analysis and deployed a sophisticated algorithm to implement our proposed model. Rigorous experiments have been conducted in a simulated virtualized cloud environment to precisely scrutinize the performance of EMDA and compare it with four cutting-edge frameworks. We have used diversified and dynamic scientific workloads to conduct simulations that prove the novelty of our proposed model. In essence, this paper outlines how dependency analysis of VM components empowers cloud infrastructure management, enhancing migration efficiency and cost-effectiveness.
