Open Access

ARTICLE

Efficient Resource Allocation in Fog Computing Using QTCS Model

M. Iyapparaja¹, Naif Khalaf Alshammari^2,*, M. Sathish Kumar¹, S. Siva Rama Krishnan¹, Chiranji Lal Chowdhary¹

1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
2 Mechanical Engineering, University of Hail, Saudi Arabia

* Corresponding Author: Naif Khalaf Alshammari. Email:

(This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)

Computers, Materials & Continua 2022, 70(2), 2225-2239. https://doi.org/10.32604/cmc.2022.015707

Received 03 December 2020; Accepted 09 January 2021; Issue published 27 September 2021

Abstract

Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

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