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 centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and non-dominated 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, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

翻译：为了便利为云用户提供具有成本效益和弹性的计算效益,虚拟机器(VM)的节能和安全配置在数据中心起着重要作用。低效率的VM定位(VMP)和多用户共享通用物理机器导致资源浪费、电力消耗过度、通信成本增加和安全破坏。为了应对上述挑战,提议了一个新的安全和多目标的虚拟机器配置(SM-VMP)框架,同时高效的VM迁移。拟议框架确保在VM之间以节能方式分配实际资源,通过减少通信延误,强调安全和及时地应用用户应用程序。VMP的实施,采用了拟议的WOGA(WOGA),其灵感来自鲸鱼的进化优化和无主排序的遗传算法。对静态和动态VMP的性能评价以及与最新艺术的比较显示,共享服务器、通信成本、电力消耗和执行时间分别显著下降至28.81%、25.7%、35.9%和82.21%,资源利用率分别提高到30.21%。