Rafiq, Adeel, Rehman, Saad, Young, Rupert, Song, Wang-Cheol, Khan, Muhammad Attique, Kadry, Seifedine and Srivastava, Gautam (2022) Knowledge defined networks on the edge for service function chaining and reactive traffic steering. Cluster Computing. pp. 1-22. ISSN 1386-7857

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Abstract

Emerging technologies such as network function virtualization and software-defined networking (SDN) have made a phenomenal breakthrough in network management by introducing softwarization. The provision of assets to each virtualized network functions autonomously as well as efficiently and searching for an optimal pattern for traffic routing challenges are still under consideration. Unfortunately, the traditional methods for estimating the desired performance indicators are insufficient for a self-driven SDN. In the last decade, a combination of machine learning and cognitive techniques construct a knowledge plane (KP) for the Internet which introduces numerous benefits to networking, like automation and recommendation. Furthermore, the inclusion of KP to the conventional three planes SDN architectures recently has added another knowledge defined networking (KDN) architecture to drive an SDN autonomously. In this article, a self-driving system has been proposed based on KDN to achieve the selection of an optimal path for the deployment of service function chaining (SFC) and reactive traffic routing among the edge clouds. Considering the limited resource of edge clouds, the proposed system also maintains a balance among edge cloud resources while orchestrating SFC resources. The graph neural network has been also applied in the proposed system to recognize the composite relationship concerning topology, traffic features, and routing patterns for accurate estimation of key performance indicators. The proposed system improves resource utilization efficiency for SFC deployment by 20%, maximum network throughput by 5%, and CPU load by 13%.

Item Type:	Article
Additional Information:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10586-022-03660-w
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
SWORD Depositor:	Mx Elements Account
Depositing User:	Mx Elements Account
Date Deposited:	19 Jul 2022 11:57
Last Modified:	19 Jul 2022 11:57
URI:	http://sro.sussex.ac.uk/id/eprint/106986

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