Abstract
With the increasing use of cloud computing by organizations, cloud data centers are proliferating to meet customers’ demands and host various applications using virtual machines installed in physical servers. Through Live Virtual Machine Migration (LVMM) methods, cloud service providers can provide improved computing capabilities for server consolidation maintenance of systems and potential power savings through a reduction in the distribution process to customers. However, Live Virtual Machine Migration has its challenges when choosing the best network path for maximizing the efficiency of resources, reducing consumption, and providing security. Most research has focused on the load balancing of resources and the reduction in energy consumption; however, they could not provide secure and optimal resource utilization. A framework has been created for sustainable data centers that pick the most secure and optimal dynamic network path using an intelligent metaheuristic algorithm, namely, the Network-aware Dynamic multi-objective Cuckoo Search algorithm (NDCS). The developed hybrid movement strategy enhances the search capability by expanding the search space and adopting a combined risk score estimation of each physical machine (PM) as a fitness criterion for ensuring security with rapid convergence compared to the existing strategies. The proposed method was assessed using the Google cluster dataset to ascertain its worthiness. The experimental results show the supremacy of the proposed method over existing methods by ensuring services with a lower total migration time, lower energy consumption, less makespan time, and secure optimum resource utilization.
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献