Uncertainty-Aware Forecasting of Computational Load in MECs Using Distributed Machine Learning: A Tokyo Case Study

Mobile Edge Clouds (MECs) address the critical needs of bandwidth-intensive, latency-sensitive mobile applications by positioning computing and storage resources at the network's edge in Edge Data Centers (EDCs). However, the diverse, dynamic nature of EDCs' resource capacities and user mobility poses significant challenges for resource allocation and management. Efficient EDC operation requires accurate forecasting of computational load to ensure optimal scaling, service placement, and migration within the MEC infrastructure. This task is complicated by the temporal and spatial fluctuations of computational load.We develop a novel MEC computational demand forecasting method using Federated Learning (FL). Our approach leverages FL's distributed processing to enhance data security and prediction accuracy within MEC infrastructure. By incorporating uncertainty bounds, we improve load scheduling robustness. Evaluations on a Tokyo dataset show significant improvements in forecast accuracy compared to traditional methods, with a 42.04% reduction in Mean Absolute Error (MAE) using LightGBM and a 34.93% improvement with CatBoost, while maintaining minimal networking overhead for model transmission.