Benchmarking management techniques for massive IIoT time series in a fog architecture Online publication date: Fri, 07-May-2021
by Sergio Di Martino; Adriano Peron; Alberto Riccabone; Vincenzo Norman Vitale
International Journal of Grid and Utility Computing (IJGUC), Vol. 12, No. 2, 2021
Abstract: Within the Industrial Internet of Things (IIoT) scenario, the online availability of a growing number of assets in factories enables the collection of vast amounts of data. Each asset produces time-series collections that must be handled with proper techniques while providing effective ingestion and retrieval performance in complex architectures, maintaining compliance with company and infrastructure boundaries. In this paper, we describe an experience in the management of massive time-series, conducted in a plant of Avio Aero. Firstly, we propose a fog-based architecture to ease the collection and analysis of these massive datasets. Then, we present the results of an empirical comparison of four DBMSs (PostgreSQL, Cassandra, MongoDB, and InfluxDB) in the ingestion and retrieval of gigabytes of real IIoT data. In particular, we tested different DBMS features under different types of queries. Results show that InfluxDB provides very good performance, but PostgreSQL can still be an interesting alternative.
Online publication date: Fri, 07-May-2021
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Grid and Utility Computing (IJGUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org