LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edge
This work addresses the need for timely and accurate performance forecasts for mobile network operators, with a focus on supporting IoT and edge devices, though it appears incremental in its approach.
The paper tackles cellular network performance forecasting by proposing LightningNet, a lightweight distributed graph-based framework that captures spatio-temporal dependencies, achieving a steady performance increase over state-of-the-art techniques while maintaining similar resource usage.
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.