DRAP-CPU: a novel vm migration approach through a dynamic prioritized resource allocation strategy

Abstract

In this study, we explore the realm of cloud computing with a particular emphasis on optimizing Virtual Machine (VM) migration, focusing primarily on the effective utilization of CPU resources. The primary objective of our research is to enhance VM migration processes by introducing a novel CPU-centric approach, thereby improving resource management, and reducing operational costs within cloud environments. We conducted extensive experimentation to develop and validate our methods. The core of our methodology revolves around advanced load balancing techniques that prioritize CPU usage. This strategic focus on CPU allocation is designed to address the common challenges in VM migration, such as resource inefficiency and high operational expenses. Our results indicate a marked improvement in VM migration efficiency compared to traditional methods. Specifically, we observed a 78% reduction in the costs associated with VM migrations, underscoring the economic viability of our approach. Additionally, our method exhibited a notable increase in the accuracy and efficiency of resource allocation during the migration process. We achieved a 100% accuracy rate in maintaining optimal load levels, a significant advancement over existing techniques. This enhancement is crucial in ensuring seamless VM operations and minimizing disruptions during migration. Our research contributes to the field of cloud computing by proposing a CPU-focused strategy for VM migration. This approach not only advances the efficiency of VM migrations but also offers substantial economic benefits. By addressing both the technical and cost-related aspects of VM migration, our study provides a comprehensive solution that could be instrumental in shaping future developments in cloud-based resource management and VM operations.