Comparative Evaluation of Machine Learning, Deep Learning, and LLM-Based Reasoning for Server Selection in 5G Multi-Access Edge Computing

Abstract

The growing demand for ultra-low-latency, high-throughput services (with 5G networks) will succeed the (proliferation of 5G networks) multi-access edge computing (MEC). Thus enabling the next phase of communication systems. In a 5G MEC architecture, the choice of when to offload a computation in an Edge Cloud server or Cloud server has serious implications for latency, resource use and quality of service. In this paper, we compare six approaches to this server-selection decision: a Random Forest, XGBoost, Support Vector Machine, Gradient Boosting, an Artificial Neural Network, and a decision engine based on a Large Language Model (Claude, Anthropic). All six methodologies were put to assess on a common, reproducible pipeline from exploratory data analysis to feature engineering to stratified data partitioning and feature standardisation, utilising the same dataset of 35,000 task instances of network and server telemetry. All of the five trained machine learning and deep learning models achieved near-optimal classification performance. The use of a three-pronged leakage diagnostic called feature-importance analysis, cross-tabulation, and ablation experiments revealed that the models relied on informative distance-linked features as opposed to inherent model capability. In terms of a no-leakage feature subset, the XGBoost model, which is the strongest classical model, achieved an accuracy of 99.70% while the Claude-based LLM engine achieved 96.0% accuracy on a noleakage comparable subset. In line with the requirement that using an existing LLM does not, by itself, demonstrate original contribution, we further propose and evaluate a Hybrid Confidence-Gated Decision Engine based on XGBoost and Claude, which escalates only cases deemed genuinely uncertain to the LLM. This hybrid engine obtained an overall accuracy of 99.90% but failed to beat the XGBoost rejected predictions on the very few escalated cases a negative result reported honestly. Meanwhile, a deeper diagnostic demonstrated that every field in our dataset, beyond the original four that we flagged, was strongly correlated with the target label. The data leakage and hybrid decision systems are examined in this multi-access edge computing assisted server selection using machine learning in 5G networks research article by Claude and more.