Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments

TL;DR

This paper introduces a cloud-based probabilistic forecasting method for resource-efficient adaptive monitoring in resource-constrained edge computing environments, reducing network overhead without reconfiguring edge devices.

Contribution

It presents a novel sampling-based approach utilizing probabilistic forecasts to adapt sampling frequencies, avoiding device reconfiguration and resource use on edge devices.

Findings

Improved resource efficiency demonstrated on a streaming dataset.

Probabilistic forecasts enable better sampling decisions.

Method outperforms existing adaptive monitoring techniques.

Abstract

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.