Learning-Based Content Delivery in 5G-Enabled Multi-Access Edge Computing
DescriptionThe demand for content such as multimedia services with high performance (e.g., ultra-low latency) requirements has increased significantly, posing heavy backhaul congestion in mobile networks. The integration of multi-access edge computing (MEC) and 5G network is an emerging solution that alleviates the backhaul congestion to meet the required network performance for user equipment (UE). Uncertainties due to user mobility, however, cause the most challenging barrier in deciding optimal content routes from edge application servers (EASs) to UEs, defined as the 5G component selection problem. To this aim, we propose a novel learning-based component selection solution for high-performance content delivery in 5G-enabled MEC that leads to minimum latency by reducing frequent handovers.
Event Type
Research Posters