Optimized Load Balancing Technique for Software Defined Network

Title

Optimized Load Balancing Technique for Software Defined Network

Creator

Kumar A.; Anand D.; Jha S.; Joshi G.P.; Cho W.

Description

Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission media loads. These loads also contribute to decreasing the performance of Software- Defined Networks. This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load. We proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network load. The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques. 2022 Tech Science Press. All rights reserved.

Source

Computers, Materials and Continua, Vol-72, No. 1, pp. 1409-1426.

Date

2022-01-01

Publisher

Tech Science Press

Subject

Load balancing; Performance enhancement; SDN; Software-defined networks

Coverage

Kumar A., Department of Computer Science and Engineering, Chandigarh University, Punjab, Mohali, 140413, India; Anand D., Department of Computer Science and Engineering, Chandigarh University, Punjab, Mohali, 140413, India; Jha S., School of Computational Sciences, CHRIST (Deemed to be University), Ghaziabad, 201003, India; Joshi G.P., Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea; Cho W., Department of Automotive ICT Convergence Engineering, Daegu Catholic University, Gyeongsan, 38430, South Korea

Rights

All Open Access; Gold Open Access

Relation

ISSN: 15462218

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.32604/cmc.2022.024970

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125396053&doi=10.32604%2fcmc.2022.024970&partnerID=40&md5=60ee45072d752c9abab13d3295995767