Network slicing management for IoT devices at home using machine learning algorithms

5G increased the speed and quality after 4G and enabled the development of new areas with the latest technology and advantages it brought. One of the groundbreaking technologies of 5g is network slicing. Network slicing simultaneously creates multiple virtual slices over the physical layer, enabling a single base station to customize those slices and improve its service according to different features and requirements. Thanks to network slicing, virtual slices with separate customizations have been created for the Internet of Things (IoT) or Ultra Reliable Low Latency Communications (URLLC), one of today's most rapidly developing technologies, enabling these areas to be integrated even faster. Resource allocation is one of the most significant areas that need improvement for network slicing. Resource allocation is the bandwidth that the base station should distribute to slices. It is critical to set this width correctly because if it is not set correctly, there may be disruptions in the users' service. Different solutions to the resource allocation problem have been presented in the literature. Among these, some studies aim to predict resource allocation with machine learning, and some papers also provide optimization with deep learning. In the thesis, the objective is to solve the problem with more than one algorithm. To solve this problem, it is attempted to get accurate results with Gurobi, a Mixed-Integer Linear Programming (MILP) solver. Afterward, Gurobi's results are compared with heuristic, Random, Balancing, Score-Based, and Reinforcement Learning solutions in different situations and scenarios. Resource allocation is the bandwidth that the base station should distribute to slices. It is critical to set this width correctly because if it is not set correctly, there may be disruptions in the users' service. Different solutions to the resource allocation problem have been presented in the literature. Among these, some studies aim to predict resource allocation with machine learning, and some papers also provide optimization with deep learning. In the thesis, the objective is to solve the problem with more than one algorithm. This thesis seeks to optimize and maximize the use of base stations and serve the maximum number of users. To solve this problem, it is attempted to get accurate results with Gurobi, a Mixed-Integer Linear Programming (MILP) solver. Afterward, Gurobi's results are compared with heuristic, Random, Balancing, Score-Based, and Reinforcement Learning solutions in different situations and scenarios.