With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments. In order to reduce the resource overhead on the network link imposed by monitoring, various methods have been discussed that either follow a filtering approach for data-emitting devices or conduct dynamic sampling based on employed prediction models. Still, existing methods are mainly requiring adaptive monitoring on edge devices, which demands device reconfigurations, utilizes additional resources, and limits the sophistication of employed models. In this paper, we propose a sampling-based and cloud-located approach that internally utilizes probabilistic forecasts and hence provides means of quantifying model uncertainties, which can be used for contextualized adaptations of sampling frequencies and consequently relieves constrained network resources. We evaluate our prototype implementation for the monitoring pipeline on a publicly available streaming dataset and demonstrate its positive impact on resource efficiency in a method comparison.

翻译：随着越来越多的计算任务被迁移至网络边缘，关键基础设施（如自动驾驶中的中间处理节点）的监测因资源受限环境而变得更为复杂。为降低监测对网络链路带来的资源开销，已有多种方法被提出，这些方法或采用数据发射设备的过滤策略，或基于预测模型进行动态采样。然而，现有方法主要需要在边缘设备上实施自适应监测，这不仅要求设备重构、占用额外资源，还限制了所使用模型的复杂度。本文提出一种基于采样的云端定位方法，该方法内部利用概率预测，从而能够量化模型不确定性，并据此实现采样频率的上下文自适应调整，进而缓解网络资源受限问题。我们基于公开流数据集对监测管道的原型实现进行了评估，并通过方法对比证明了其在资源效率方面的积极影响。