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被转移到网络边缘,由于通常资源有限的环境,监测关键基础设施(如自主驾驶的中间加工节点)的工作更加复杂。为了减少监测所强加的网络连接的资源间接费用,已经讨论了各种方法,这些方法要么采用数据排放装置的过滤方法,要么根据所采用的预测模型进行动态抽样。但现有方法主要要求对边缘装置进行适应性监测,这些装置要求设备重组,利用额外资源,并限制所用模型的精密性。在本文件中,我们提议采用基于取样和云层分配的方法,在内部利用概率预测,从而提供量化模型不确定性的手段,用于根据具体情况调整取样频率,从而缓解网络资源的制约。我们评估我们用于监测公开流数据集的管道的原型执行情况,并以方法比较的方式表明其对资源效率的积极影响。