Vol.67, No.1, 2021, pp.849-875, doi:10.32604/cmc.2021.014576
Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering
  • Jehad Ali, Byeong-hee Roh*
Department of Computer Engineering and Department of AI Convergence Network, Suwon, 16499, Korea
* Corresponding Author: Byeong-hee Roh. Email:
Received 30 September 2020; Accepted 24 October 2020; Issue published 12 January 2021
The controller is indispensable in software-defined networking (SDN). With several features, controllers monitor the network and respond promptly to dynamic changes. Their performance affects the quality-of-service (QoS) in SDN. Every controller supports a set of features. However, the support of the features may be more prominent in one controller. Moreover, a single controller leads to performance, single-point-of-failure (SPOF), and scalability problems. To overcome this, a controller with an optimum feature set must be available for SDN. Furthermore, a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN. Herein, leveraging an analytical network process (ANP), we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster (HCPC) of the highly ranked controller computed using the ANP, evaluating their performance for the OS3E topology. The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS. Moreover, the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering (DCC) schemes in terms of several performance metrics i.e., delay, jitter, throughput, load balancing, scalability and CPU (central processing unit) utilization.
Quality-of-service; software-defined networking; controller; hierarchical control plane clustering; scalability
Cite This Article
J. Ali and B. Roh, "Quality of service improvement with optimal software-defined networking controller and control plane clustering," Computers, Materials & Continua, vol. 67, no.1, pp. 849–875, 2021.
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