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.

我们提出了 LightningNet，​​一种轻量级的分布式基于图的框架，用于预测移动网络性能，能够捕捉网络流量中出现的时空依赖关系。LightningNet 在保持类似资源使用配置的同时，相对于现有技术具有稳定的性能提升。我们的架构理念还在支持物联网和边缘设备方面表现优秀，根据与 NVIDIA Jetson 的性能实验表明，我们具有更先进的技术水平。

LightningNet：面向边缘的分布式基于图的蜂窝网络性能预测

LightningNet: Distributed Graph-based Cellular Network Performance  Forecasting for the Edge

Our goal is to improve reliability of Machine Learning (ML) systems deployed
in the wild. ML models perform exceedingly well when test examples are similar
to train examples. However, real-world applications are required to perform on
any distribution of test examples. Current ML systems can fail silently on test
examples with distribution shifts. In order to improve reliability of ML models
due to covariate or domain shift, we propose algorithms that enable models to:
(a) generalize to a larger family of test distributions, (b) evaluate accuracy
under distribution shifts, (c) adapt to a target distribution. We study causes
of impaired robustness to domain shifts and present algorithms for training
domain robust models. A key source of model brittleness is due to domain
overfitting, which our new training algorithms suppress and instead encourage
domain-general hypotheses. While we improve robustness over standard training
methods for certain problem settings, performance of ML systems can still vary
drastically with domain shifts. It is crucial for developers and stakeholders
to understand model vulnerabilities and operational ranges of input, which
could be assessed on the fly during the deployment, albeit at a great cost.
Instead, we advocate for proactively estimating accuracy surfaces over any
combination of prespecified and interpretable domain shifts for performance
forecasting. We present a label-efficient estimation to address estimation over
a combinatorial space of domain shifts. Further, when a model's performance on
a target domain is found to be poor, traditional approaches adapt the model
using the target domain's resources. Standard adaptation methods assume access
to sufficient labeled resources, which may be impractical for deployed models.
We initiate a study of lightweight adaptation techniques with only unlabeled
data resources with a focus on language applications.

通过培训算法来提高对于机器学习领域的可靠性，从而应对领域的变化，提高算法的性能，预测其表现并轻量级适应应用领域。