Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments
This addresses monitoring challenges for resource-constrained edge computing applications like autonomous driving, though it appears incremental compared to existing adaptive methods.
The paper tackles the problem of resource-intensive monitoring in edge computing environments by proposing a cloud-located probabilistic forecasting approach that adapts sampling frequencies based on model uncertainty, demonstrating improved resource efficiency in evaluations.
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.