An intelligent model for supporting edge migration for virtual function chains in next generation internet of things

Tsakanikas, V, Dagiuklas, T, Iqbal, M, Wang, X and Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149, 2023. An intelligent model for supporting edge migration for virtual function chains in next generation internet of things. Scientific Reports, 13 (1): 1063. ISSN 2045-2322

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Abstract

The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Tsakanikas, V., Dagiuklas, T., Iqbal, M., Wang, X. and Mumtaz, S.
Publisher:	Springer Science and Business Media LLC
Date:	2023
Volume:	13
Number:	1
ISSN:	2045-2322
Identifiers:	Number Type 10.1038/s41598-023-27674-5 DOI 1732515 Other
Rights:	© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	17 Feb 2023 11:56
Last Modified:	17 Feb 2023 11:56
URI:	https://irep.ntu.ac.uk/id/eprint/48348

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