IHSF: An Intelligent Solution for Improved Performance of Reliable and Time-Sensitive Flows in Hybrid SDN-Based FC IoT Systems

Muhammad Ibrar, Lei Wang, Gabriel Miro Muntean, Jenhui Chen*, Nadir Shah, Aamir Akbar

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

39 Scopus citations

Abstract

The integration of software-defined networking (SDN) into legacy networks causes both operational and deployment issues. In this context, this article proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in hybrid SDN-based fog computing IoT systems (IHSF). The proposed IHSF approach has three solutions: 1) a novel algorithm to deploy SDN switches between legacy switches to improve network observability; 2) a {K}-nearest neighbor regression algorithm to predict in real time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance; and 3) a reliable and time-sensitive deep deterministic policy gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio.

Original languageEnglish
Article number9199898
Pages (from-to)3130-3142
Number of pages13
JournalIEEE Internet of Things Journal
Volume8
Issue number5
DOIs
StatePublished - 01 03 2021

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Fog computing (FC)
  • IoT
  • hybrid software-defined networking (SDN)
  • link failure
  • machine learning

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