Open Access

ARTICLE

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

by Muchang Rao, Hang Qin^*

School of Computer Science, Yangtze University, Jingzhou, 434000, China

* Corresponding Author: Hang Qin. Email:

(This article belongs to the Special Issue: Multi-Service and Resource Management in Intelligent Edge-Cloud Platform)

Computers, Materials & Continua 2024, 79(2), 2647-2672. https://doi.org/10.32604/cmc.2024.050380

Received 05 February 2024; Accepted 02 April 2024; Issue published 15 May 2024

Abstract

More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment framework, followed by the formulation of a mathematical model for task scheduling. Lastly, we introduce an enhanced hybrid Equilibrium Optimizer (EHEO) tailored for AIoT task scheduling. The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines. The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms, encompassing metrics like makespan, total energy consumption, success rate, and average waiting time. Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics. Notably, in the most favorable conditions, EHEO significantly diminishes both the makespan and energy consumption by approximately 50% and 35.5%, respectively, compared to the second-best performing approach, which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

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Keywords

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