Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments

Scheinert, D., Zadeh Aghdam, B. S., Becker, S., Kao, O. and Thamsen, L. (2023) Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments. In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), Osaka, Japan, 17-20 Dec 2022, pp. 4583-4588. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10021129)

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.

• Item Type: Conference Proceedings
• Additional Information: This work has been supported through grants by the German Federal Ministry of Education and Research (BMBF) as BIFOLD (funding mark 01IS18025A) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as FONDA (Project 414984028, SFB 1404).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Thamsen, Dr Lauritz
• Authors: Scheinert, D., Zadeh Aghdam, B. S., Becker, S., Kao, O., and Thamsen, L.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781665480451
• Copyright Holders: Copyright © 2022 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Related URLs

• Organisation