Data-Driven Emulation of Mobile Access Networks

Network monitoring is fundamental to understand network evolution and behavior. However, monitoring studies have the main limitation of running new experiments when the phenomenon under analysis is over e.g., congestion. To overcome this limitation, network emulation is of vital importance for network testing and research experiments either in wired and mobile networks. When it comes to mobile networks, the variety of technical characteristics, coupled with the opaque network configurations, make realistic network emulation a challenging task. In this paper, we address this issue leveraging a large scale dataset composed of 500M network latency measurements in Mobile BroadBand networks. By using this dataset, we create 51 different network latency profiles based on the Mobile BroadBand operator, the radio access technology and signal strength. These profiles are then processed to make them compatible with the tc-netem emulation tool. Finally, we show that, despite the limitation of current tc-netem emulation tool, Generative Adversarial Networks are a promising solution used to create realistic temporal emulation. We believe that this work could be the first step toward a comprehensive data-driven network emulation. For this, we make our profiles and codes available to foster further studies in these directions.