Optimizing Network Slices: A Comparative Analysis of Allocation Algorithms for 5G Environments

In the realm of 5G networking, the optimization of user allocation through network slicing stands as a critical challenge, with the potential to substantially enhance the Quality of Service (QoS). This study examines three AI-based allocation algorithms—Simulated Annealing, which begins with a Randomized algorithm, Greedy, and Local Search with Hill Climbing—to efficiently distribute network resources. Next, we compare the algorithms for different user densities to understand how well each one can handle the situation at hand in terms of balance in allocation, consumption (time and memory) and complexity. Our research advances beyond conventional allocation techniques by offering different solutions for different needs thus improving QoS through the alignment of user demands with network capacity.