Robust Access Point Clustering in Edge Computing Resource Optimization

Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency bandwidth. In this work, we tackle problem of clustering access points in MEC environments, introducing a set models be deployed at pre-provisioning phase. We go through extensive simulations real-world traffic demands evaluate performance proposed solutions. addition, show how hosts capacity violation can decreased when integrating into orchestration model, investigating solution accuracy applied held-out users demands. The obtained results that our approach outperforms two state-of-the-art algorithms, reducing memory usage execution time, 46% 50%, respectively, comparison baseline algorithm. It surpasses methods gaining control over for different maximum achieved occupancy levels hosts.