Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks

Abstract

Mobile Edge Computing (MEC) in 5G networks has emerged as a promising technology to enable efficient and low-latency services for mobile users. In this paper, we present a novel synthetic data generation approach tailored for evaluating MEC in 5G networks. Our methodology incorporates resource-efficient techniques to generate realistic synthetic datasets that capture the spatio-temporal patterns of mobile traffic and user behavior. By leveraging advanced modeling techniques, including multi-head attention and bidirectional LSTM, we accurately model the complex dependencies in the data while optimizing computational resources. The proposed synthetic data generator enables the creation of diverse datasets that closely resemble real-world scenarios, facilitating the evaluation of MEC performance and optimizing resource utilization. Through extensive experiments and evaluations, we demonstrate the effectiveness of our approach in enabling accurate assessments of MEC in 5G networks. Our work contributes to the field by providing a robust methodology for synthetic data generation specifically tailored for MEC evaluation, addressing the need for resource-efficient evaluation frameworks in the context of emerging technologies. The results of our study provide valuable insights for the design and optimization of MEC systems in real-world deployments.