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Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering - 2021

Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

Research paper on Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

Research Area:  Software Defined Networks

Abstract:

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.

Keywords:  
Quality-of-service; software-defined networking; controller; hierarchical control plane clustering; scalability

Author(s) Name:   Jehad Ali, Byeong-hee Roh

Journal name:  Computers, Materials & Continua

Conferrence name:  

Publisher name:  Tech Science Press

DOI:  10.32604/cmc.2021.014576

Volume Information:  vol. 67, no.1, pp. 849–875, 2021.