A new Gaussian Black-winged Kite Algorithm for Task Scheduling Optimization of Industrial IoT Applications in a Fog Computing Environment

Abstract

As a result of the increase in industrial Internet of Things (IoT) applications, fog computing (FC) has become a major area of research. A decentralized computing system called fog computing extends cloud computing to the network's edge. The cloud allows for real-time insights and analysis by processing and storing enormous volumes of data produced by IoT devices. Consequently, the task scheduling technique in cloud computing is crucial. A number of metrics, such as makespan, resource utilization, and energy consumption, must be optimized for FC to function efficiently. This paper proposes a novel metaheuristic optimization technique called the Gaussian Black-winged Kite Algorithm (GBKA) to address task scheduling optimization of industrial IoT applications in a fog computing environment. The proposed algorithm employs Gaussian mutation, and the migration patterns and attack style of the black-winged kite serve as the inspiration for the proposed GBKA. The algorithm is designed to balance exploration of the search space and exploitation of the best solutions, avoiding local optima and improving energy efficiency. The Google Cloud Jobs dataset (GoCJ) with varying task sizes is used to validate the proposed algorithm. An analysis has been conducted to compare the performance of the proposed algorithm with the standard Black-winged Kite Algorithm (BKA) and metaheuristic algorithms like Dragonfly Algorithm (DA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). Experimental results show that GBKA reduces energy and makespan by an average of 7.26% and 9.32%, respectively. Additionally, it attains optimal resource utilization with an average overall improvement of 8.54%.