The improvement of the distributed computing efficiency in cloud–fog environments using data mining and metaheuristic algorithms

• Published in: The Journal of supercomputing Vol. 81; no. 4; p. 506
• Main Authors: Mabadifar, Tahmineh, Attarzadeh, Iman, Mahdipour, Ebrahim
• Format: Journal Article
• Language: English
• Published: New York Springer Nature B.V 01.03.2025
• Subjects: Algorithms; Cloud computing; Clustering; Computer centers; Computing time; Data mining; Data processing; Distributed processing; Edge computing; End users; Energy consumption; Exploitation; Heuristic; Heuristic methods; Internet of Things; Mathematical functions; Optimization; Particle swarm optimization; Response time; Scheduling; Smart devices; Smartphones; Support vector machines; Task scheduling
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-06847-7

The Internet of Things highly depends on computing environments like cloud computing to process and store information. The use of cloud computing by smart devices leads to challenges such as delay and increased energy consumption of sensors. A primary solution to the mentioned problems is fog computing. Task scheduling is the most critical issue that significantly affects improving the performance of cloud–fog systems. Task scheduling is an NP-hard problem, and applying data mining methods and metaheuristic algorithms to obtain optimal solutions in a reasonable computing time is a fundamental requirement. This paper proposes a new model based on metaheuristic algorithms using the combination of golden jackal optimization (GJO) and beluga whale optimization (BWO) algorithm called GJOBWO to solve the task scheduling problem in a cloud–fog environment. In the hybrid model, the BWO algorithm is used to solve the issues of the GJO algorithm, such as getting stuck in the local optimum and imbalance between the exploration and exploitation stages. Performing the exploration and exploitation steps is essential because correct execution may lead to efficient solutions. Also, the k-means algorithm inspired by clustering is used to prioritize tasks. The evaluation of the hybrid model has been done using continuous optimization functions and task scheduling problems. First, the hybrid model has been implemented on 68 standard functions and compared with particle swarm optimization (PSO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), ant lion optimizer (ALO), and GJO and BWO algorithms. Then, the hybrid model has been tested on the task scheduling problem and compared with WOA, GJO, and BWO algorithms. The results show that the hybrid model has effectively minimized the makespan rate and the degree of imbalance. Also, the average improvement percentage of the combined model based on the PIR criterion compared to the four algorithms SCA, WOA, GJO, and BWO is around 3.24.