TY - JOUR
T1 - IHSF
T2 - An Intelligent Solution for Improved Performance of Reliable and Time-Sensitive Flows in Hybrid SDN-Based FC IoT Systems
AU - Ibrar, Muhammad
AU - Wang, Lei
AU - Muntean, Gabriel Miro
AU - Chen, Jenhui
AU - Shah, Nadir
AU - Akbar, Aamir
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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.
AB - 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.
KW - Fog computing (FC)
KW - hybrid software-defined networking (SDN)
KW - IoT
KW - link failure
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85101703478&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.3024560
DO - 10.1109/JIOT.2020.3024560
M3 - Article
AN - SCOPUS:85101703478
SN - 2327-4662
VL - 8
SP - 3130
EP - 3142
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 5
M1 - 9199898
ER -