An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing

Ibrahim Attiya, Laith Abualigah, Doaa Elsadek, Samia Allaoua Chelloug and Mohamed Abd Elaziz
Additional contact information
Ibrahim Attiya: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
Doaa Elsadek: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Samia Allaoua Chelloug: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohamed Abd Elaziz: Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt

Mathematics, 2022, vol. 10, issue 7, 1-18

Abstract: The cloud computing paradigm is evolving rapidly to address the challenges of new emerging paradigms, such as the Internet of Things (IoT) and fog computing. As a result, cloud services usage is increasing dramatically with the recent growth of IoT-based applications. To successfully fulfill application requirements while efficiently harnessing cloud computing power, intelligent scheduling approaches are required to optimize the scheduling of IoT application tasks on computing resources. In this paper, the chimp optimization algorithm (ChOA) is incorporated with the marine predators algorithm (MPA) and disruption operator to determine the optimal solution to IoT applications’ task scheduling. The developed algorithm, called CHMPAD, aims to avoid entrapment in the local optima and improve the exploitation capability of the basic ChOA as its main drawbacks. Experiments are conducted using synthetic and real workloads collected from the Parallel Workload Archive to demonstrate the applicability and efficiency of the presented CHMPAD method. The simulation findings reveal that CHMPAD can achieve average makespan time improvements of 1.12–43.20% (for synthetic workloads), 1.00–43.43% (for NASA iPSC workloads), and 2.75–42.53% (for HPC2N workloads) over peer scheduling algorithms. Further, our evaluation results suggest that our proposal can improve the throughput performance of fog computing.

Keywords: chimp optimization algorithm; marine predators algorithm; cloud computing; fog computing; task scheduling; makespan; metaheuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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