Open Access

ARTICLE

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Anu^*, Anita Singhrova

Deenbandhu Chhotu Ram University of Science and Technology, Murthal, 131039, Haryana, India

* Corresponding Author: Anu. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 199-219. https://doi.org/10.32604/iasc.2023.035389

Received 18 August 2022; Accepted 22 December 2022; Issue published 29 April 2023

Abstract

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things (IoT) applications in the proximity of the network. Fog computing offers computational and storage services between cloud and terminal devices. However, an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services. A large number of heterogeneous shareable resources makes fog computing a complex environment. In the sight of these issues, this paper has proposed an efficient levy flight firefly-based resource allocation technique. The levy flight algorithm is a metaheuristic algorithm. It offers high efficiency and success rate because of its longer step length and fast convergence rate. Thus, it treats global optimization problems more efficiently and naturally. A system framework for fog computing is presented, followed by the proposed resource allocation scheme in the fog computing environment. Experimental evaluation and comparison with the firefly algorithm (FA), particle swarm optimization (PSO), genetic algorithm (GA) and hybrid algorithm using GA and PSO (GAPSO) have been conducted to validate the effectiveness and efficiency of the proposed algorithm. Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service (QoS). The proposed algorithm reduces average waiting time, average execution time, average turnaround time, processing cost and energy consumption and increases resource utilization and task success rate compared to FA, GAPSO, PSO and GA.

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