IoT Workflow Scheduling Using Intelligent Arithmetic Optimization Algorithm in Fog Computing

Author:

Abd Elaziz Mohamed12345ORCID,Abualigah Laith67ORCID,Ibrahim Rehab Ali1ORCID,Attiya Ibrahim12ORCID

Affiliation:

1. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

2. Academy of Scientific Research and Technology (ASRT), 101 Qasr Al Aini St., Cairo PO Box 11516, Cairo, Egypt

3. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan

4. School of Computer Sciences, Universiti Sains Malaysia, Gelugor, Pulau Pinang 11800, Malaysia

5. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, UAE

6. Faculty of Computer Science Engineering, Galala University, Suze 435611, Egypt

7. School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk 634050, Russia

Abstract

Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to boost the quality of services (QoSs) needed by many applications. However, the availability of continuous computing resources on fog computing servers is one of the restrictions for IoT applications since transmitting the large amount of data generated using IoT devices would create network traffic and cause an increase in computational overhead. Therefore, task scheduling is the main problem that needs to be solved efficiently. This study proposes an energy-aware model using an enhanced arithmetic optimization algorithm (AOA) method called AOAM, which addresses fog computing’s job scheduling problem to maximize users’ QoSs by maximizing the makespan measure. In the proposed AOAM, we enhanced the conventional AOA searchability using the marine predators algorithm (MPA) search operators to address the diversity of the used solutions and local optimum problems. The proposed AOAM is validated using several parameters, including various clients, data centers, hosts, virtual machines, tasks, and standard evaluation measures, including the energy and makespan. The obtained results are compared with other state-of-the-art methods; it showed that AOAM is promising and solved task scheduling effectively compared with the other comparative methods.

Funder

Academy of Scientific Research and Technology

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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