LightningNet: Distributed Graph-based Cellular Network Performance
  Forecasting for the Edge
Konstantinos Zacharopoulos Georgios Koutroumpas Ioannis Arapakis Konstantinos Georgopoulos Javad Khangosstar Sotiris Ioannidis
The cellular network plays a pivotal role in providing Internet access, since
it is the only global-scale infrastructure with ubiquitous mobility support. To
manage and maintain large-scale networks, mobile network operators require
timely information, or even accurate performance forecasts. In this paper, we
propose LightningNet, a lightweight and distributed graph-based framework for
forecasting cellular network performance, which can capture spatio-temporal
dependencies that arise in the network traffic. LightningNet achieves a steady
performance increase over state-of-the-art forecasting techniques, while
maintaining a similar resource usage profile. Our architecture ideology also
excels in the respect that it is specifically designed to support IoT and edge
devices, giving us an even greater step ahead of the current state-of-the-art,
as indicated by our performance experiments with NVIDIA Jetson.