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Top1. Introduction
Cloud computing is a service-oriented architecture that provided computing power and data storage. However, the large-scale application has increased the amount of workloads and the number of tasks in cloud computing (Mateusz, et al., 2015;Huang, et al., 2018). Some computing nodes in the cloud is underutilized due to the uneven scale in the task and the different computing capacities of nodes, whereas other nodes leads to unbalanced load distribution due to overload. Thus, the load needs to be spread across the computing nodes to improve the satisfaction of the user and to take the advantage of the cloud computing system (Milani and Navimipour, 2016;Huang, et al., 2018). Cloud computing has gained the attention of the industrial (Desogus M., and Casu E., 2019; Desogus M., and Venturi B. 2019), medical (Ferrari, et al., 2015). (Beno, et al., 2014), digital forensics (Barone & Maggio 2019) and academic communities. The local data centers are replaced by the individuals and enterprises, which are permitted to outsource the significant quantity of data to the cloud (Kumar & Vimala, 2020). The cloud users utilized the variety of computing services, which are presented using the public cloud (Ning, et al., 2017). Cloud computing allows the utilization of the computing infrastructure at various levels of abstraction and the availability of the on-demand services available in the computer networks and the internet. The merging of cloud computing and artificial intelligence (AI) (Gupta, et al., 2020) is mainly used in the medical applications (Gupta, et al., 2020 ; Gupta, et al., 2020). In cloud computing, the metaheuristic-based optimization techniques (Gupta, et al., 2020) are used to achieve near optimal solutions within a reasonable time. Due to the availability at lower cost and greater flexibility, cloud computing has gaining a lot of attention (Jansen and Grance, 2011).
In virtual machine (VM) replacement process, the most appropriate server is selected from the large cloud data centers for deploying the newly-created VMs. Efficient energy consumption and the load balance is achieved through the VM migration technology. Besides providing secure and efficient computing resource containers, the VM helps in the migration of the resources among the multiple physical machines smoothly. The running VM is considered by the VM migration, which moves to various physical machines. The VM migration should be clear to the applications that are moving on the operating system, guest operating system and remote clients of the VM. The parties that are involved should be shown that the VM did not change the location (Nelson, et al., 2005). The VM is mapped with the physical resources using the VM monitors like Xen and the mapping is not visible to the users in the cloud. As similar to the general the cloud computing resources, the manufacturing knowledge and the computational resources are virtualized. The system independent VMs are created by mapping the manufacturing hardware. The allocation, coordination and the communication with the lower level devices of the VM is performed by the Virtualization managers (for instance, VM Manager and VM Monitor ;Xu, 2012).