A FUTURE TREND ON 5G NETWORK SUB-SLICING TECHNIQUES FOR MACHINE LEARNING ALGORITHMS

One of the primary objectives of 5G networks is to meet the need for vertical services. Artificial reality/virtual reality, electronic health records, live video streaming, robotics, driverless vehicles, and many other applications might benefit from 5G networks. Optimal 5G Network Sub-Slicing Automation (ONSSA) is a novel machine learning framework introduced in this paper for autonomous and dynamic 5G network slice processing. After analyzing historical data and the present state of the network, the framework uses the LazyPredict module to automatically choose the most effective unsupervised learning algorithms. For systems that run concurrently at numerous infrastructure layer levels, network slicing poses very difficult and critical security challenges. Some security challenges that need attention are as follows: It is possible to implement security-based interslice, interslice, and multidomain. The data is vulnerable to hacking in situations when a conventional network based on machine learning is used. Accordingly, Data Communication Network as a Service (DCNaaS), is a whole new service model for Mobile Network Organizations (MNOs). To evaluate the framework, we used Python for dynamic testing and Anaconda Spyder for machine learning implementation. To address the slice allocation problem, this research makes use of datasets and machine learning techniques. We have evaluated the slice allocation approach using many ML models. We demonstrated in the simulation that the proposed method correctly allocates the best slice for service.