Improvement of QoS in an IoT Ecosystem by Integrating Fog Computing and SDN

Author:

Ahammad Ishtiaq1,Khan Md. Ashikur Rahman1,Salehin Zayed Us1,Uddin Main1,Soheli Sultana Jahan1

Affiliation:

1. Noakhali Science and Technology University, Bangladesh

Abstract

The internet of things (IoT) creates immense volume of objects online. But cloud computing isn't suited to environmental demands. Hence, fog computing (FC) emerged which shifts the computation load into edge fog devices. However, FC also faces some obstacles which can be mitigated by software-defined networking (SDN). By combining SDN and FC, the network form can overcome almost all cloud limitations and can boost QoS. Within this article, architecture is proposed by combining SDN and FC to improve QoS for IoT ecosystem. With the architecture, an algorithm is propounded based on virtual partition. Then a use case is presented and evaluated through iFogSim simulator. The result shows a significant improvement of several QoS parameters in the execution of fog with SDN compared to the cloud-only execution. The results also show better results for energy consumption, network use (212.21% reduction), and latency (275.9% reduction) compared with previous similar use case.

Publisher

IGI Global

Subject

General Medicine

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fog Computing Complete Review: Concepts, Trends, Architectures, Technologies, Simulators, Security Issues, Applications, and Open Research Fields;SN Computer Science;2023-10-04

2. Machine learning enabled network and task management in SDN based Fog architecture;Computers and Electrical Engineering;2023-05

3. ImmuneGAN: Bio-inspired Artificial Immune System to Secure IoT Ecosystem;Lecture Notes in Networks and Systems;2023

4. Cost-Effective Spot Instances Provisioning Using Features of Cloud Markets;International Journal of Cloud Applications and Computing;2022-11-30

5. A State-of-the-Art Survey and Taxonomy for Load Balancing Metrics in SDN Networks;2022 2nd International Conference on Advances in Engineering Science and Technology (AEST);2022-10-24

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3