A decentralized approach for determining configurator placement in dynamic edge networks

Ilir Murturi, Mohammadreza Barzegaran, Schahram Dustdar

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In today's IoT infrastructures, increasingly newly added computational resources at the edge of a network are added to acquire faster response and increased privacy. Such edge networks bring an opportunity for deploying edge application services in proximity to IoT domains and the end-users. In this paper, we consider the problem of utilizing various computational resources established by multiple heterogeneous edge devices in dynamic edge networks. A new lightweight decentralized mechanism (i.e., configurator) is required to monitor an edge infrastructure to enable deploying, orchestrating, and monitoring edge applications at the edge. In this setting, one critical task is to determine the node where the configurator should be placed (deployed) and run (executed) at the edge. In this paper, we propose an efficient approach that solves the configurator's placement problem on the most suited edge device in a given dynamic edge network. Our approach supports the system coping with the dynamicity and uncertainty of the environment and adapts based on the configurator's service quality. We discuss the architecture, processes of the approach, and the simulations we conducted to validate its feasibility.

Original languageEnglish
Title of host publicationProceedings of IEEE 2nd International Conference on Cognitive Machine Intelligence
PublisherIEEE
Publication dateOct 2020
Pages147-156
Article number9319378
ISBN (Electronic)9781728141442
DOIs
Publication statusPublished - Oct 2020
Event2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020 - Virtual, Atlanta, United States
Duration: 1 Dec 20203 Dec 2020

Conference

Conference2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020
CountryUnited States
CityVirtual, Atlanta
Period01/12/202003/12/2020
SeriesProceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020

Bibliographical note

Funding Information:
Research partially supported by the Smart Communities and Technologies (Smart CT) at TU Vienna and the EU H2020 Marie Skłodowska-Curie grant No. 764785 FORA–Fog Computing for Robotics and Industrial Automation.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Decentralized
  • Edge Computing
  • Internet of Things
  • Resource Management

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