Virtual machines (VMs) are preferred by the majority of organizations due to their high performance. VMs allow for
reduced overhead with multiple systems running from the same console at the same time. A physical server is a baremetal
system whose hardware is controlled by the host operating system. A physical server runs on a single instance
of OS and application. A virtual server or virtual machine encapsulates the underlying hardware and networking
resources. With the existing physical server, it is difficult to migrate the tasks from one platform to another platform
or to a datacentre. Centralized security is difficult to setup. But with Hypervisor the virtual machine can be deployed,
for instance, with automation. Virtualization cost increases as well as a decrease in hardware and infrastructure space
costs. We propose an efficient Azure cloud framework for the utilization of physical server resources at remote VM
servers. The proposed framework is implemented in two phases first by integrating physical servers into virtual ones
by creating virtual machines, and then by integrating virtual servers into cloud service providers in a cost-effective
manner. We create a virtual network in the Azure datacenter using the local host physical server to set up the various
virtual machines. Two virtual machine instances, VM1 and VM2, are created using Microsoft Hyper-V with the server
Windows 2016 R. The desktop application is deployed and VM performance is monitored using the PowerShell script.
Tableau is used to evaluate the physical server functionality of the worksheet for the deployed application.
The proposed Physical to Virtual to Cloud model (P2V2C) model is being tested, and the performance result shows
that P2V2C migration is more successful in dynamic provisioning than direct migration to cloud platform infrastructure.
The research work was carried out in a secure way through the migration process from P2V2C.
Keywords: Physical server, Virtual machine, Azure cloud, PowerShell, Tableau, Synchronization, Performance metrics,
File sharing, Key generation