A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center

Abstract

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data center. The inefficient VM placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased intercommunication cost, and security breaches. To address the aforementioned challenges, a novel secure and multiobjective VMP (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs, which emphasizes secure and timely execution of user application by reducing intercommunication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state of the arts observed a notable reduction in shared servers, intercommunication cost, power consumption, and execution time up to 28.81%, 25.7%, 35.9%, and 82.21%, respectively with increased resource utilization up to 30.21%.