Quality-of-control-aware scheduling of communication in TSN-based fog computing platforms using constraint programming

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Abstract

In this paper we are interested in real-time control applications that are implemented using Fog Computing Platforms consisting of interconnected heterogeneous Fog Nodes (FNs). Similar to previous research and ongoing standardization efforts, we assume that the communication between FNs is achieved via IEEE 802.1 Time Sensitive Networking (TSN). We model the control applications as a set of real-time streams, and we assume that the messages are transmitted using time-sensitive traffic that is scheduled using the Gate Control Lists (GCLs) in TSN. Given a network topology and a set of control applications, we are interested to synthesize the GCLs for messages such that the quality-of-control of applications is maximized and the deadlines of real-time messages are satisfied. We have proposed a Constraint Programming-based solution to this problem, and evaluated it on several test cases.
Original languageEnglish
Title of host publication2nd Workshop on Fog Computing and the IoT
PublisherSchloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany
Publication date2020
Pages3:1--3:9
ISBN (Print)978-3-95977-144-3
DOIs
Publication statusPublished - 2020
Event2nd Workshop on Fog Computing and the IoT - Hilton Sydney, Sydney, Australia
Duration: 21 Apr 202021 Apr 2020

Conference

Conference2nd Workshop on Fog Computing and the IoT
LocationHilton Sydney
CountryAustralia
CitySydney
Period21/04/202021/04/2020
SeriesOpen Access Series in Informatics
Volume80
ISSN2190-6807

Keywords

  • TSN
  • Fog Computing
  • Constraint Programming
  • Quality of Control

Cite this

Barzegaran, M., Zarrin, B., & Pop, P. (2020). Quality-of-control-aware scheduling of communication in TSN-based fog computing platforms using constraint programming. In 2nd Workshop on Fog Computing and the IoT (pp. 3:1--3:9). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany. Open Access Series in Informatics, Vol.. 80 https://doi.org/10.4230/oasics.fog-iot.2020.3