A Comparative Analysis of Machine Learning Techniques for Enhanced Resource Management in Multi-Access Edge Computing

The Internet of Things (IoT) merges physical and virtual realms, whereas Mobile Access Edge Computing (MEC) facilitates various IoT applications in this context. Machine Learning (ML) can predict the optimal amount of resources, optimizing resource usage in edge environments with limited resources. However, selecting an appropriate approach is chal-lenging because of the available techniques and algorithms. This study offers an in-depth analysis of several algorithms applied in diverse contexts for predictive autoscaling edge IoT applications. Using open datasets, we comprehensively compared these algorithms' performance across multiple scenarios commonly encountered at the edge of the network. We assessed their efficacy in univariate/multivariate, one-step/multistep forecasting, and regression/classification tasks. Our findings indicate no one-size-fits-all solution because different algorithms are more suitable for distinct scenarios.