Research Area:  Software Defined Networks

Abstract:

Driven by the emerging advanced information and communication technologies, e.g., artificial intelligence, 5G wireless communications, big data analytics, etc., industrial Internet serves as a key enabling technology to realize intelligent manufacturing, and has been attracting considerable attentions from academia and industry. However, the traditional industrial networks can hardly satisfy the quality of service (QoS) requirements for some mission-critical industrial applications (e.g., fault detection, advanced control, remote monitoring, predictive maintenance, etc.) due to network heterogeneity, traffic congestion, dynamic end-to-end latency, reliability issues, and so on. The emerging software-defined networking (SDN) has been considered as a promising architecture to improve the QoS of industrial applications by flexibly decoupling the control and data planes to control the network behaviours centrally. Owing to economy and policy considerations, a realistic solution is to incrementally deploy SDN in industrial networks instead of fully replacing traditional industrial routers with SDN-enabled switches. In this article, we consider a hybrid Industrial network consisting of conventional routers (e.g., running OSPF protocol) and SDN-enabled switches (e.g., running OpenFlow protocol), and propose an intelligent QoS-aware forwarding strategy to improve the QoS of industrial applications, by utilizing a single path minimum cost forwarding scheme and a K-path partition algorithm for multipath forwarding. Simulation results demonstrate that the proposed scheme not only guarantees the QoS requirements of industrial services, but also efficiently utilizes bandwidth resources by balancing traffic load in the SDN/OSPF hybrid industrial Internet.

Keywords:  
Artificial intelligence
quality of service (QoS)
software-defined networking (SDN)
SDN/OSPF hybrid industrial Internet

Author(s) Name:  Yuanguo Bi; Guangjie Han; Chuan Lin; Yan Peng; Huayan Pu; Yazhou Jia

Publisher name:  IEEE

DOI:  10.1109/TII.2019.2946045

Volume Information:  Volume: 16, Issue: 2, February 2020, Page(s): 1395 - 1405

Paper Link:   https://ieeexplore.ieee.org/document/8863947