An Efficient Approach for Remote Machine Access Using Machine Learning
In today’s IT sector, as the data is increasing to a greater extent, so a variety of software architectures, development frameworks, software services, software maintenance and support systems are provided by the IT firms to handle the computation in an easy manner. The major challenge faced by the IT firms is the feasibility of the company/firm representative to approach the client in person to solve the problem faced by them. Hence, in order to solve it, a remote host connection is needed which can access the client machine. The service provider can provide the clients with the solution through remote host access. There are many remote host access applications available in the market to provide such facility for the company's clients like TightVNC, AnyDesk, Remote Desktop Manager and Chrome Remote Desktop. But the issue faced by the existing system is in terms of software usage like poor bandwidth, security issues, performance issues, scalability, reliability, and platform dependency. Our paper proposed the integration of the machine learning approach with the remote access machines which can monitor the parameters of the remote machine-like bandwidth, performance, and can solve the security issues. The graphical representation with respect to the parameters like CPU utilization, network connectivity, GPU utilization, security in connectivity, devices health is depicted thereby proving that how the proposed work outshines the limitations of the existing work.