IoT Workflow Scheduling Using Intelligent Arithmetic Optimization Algorithm in Fog Computing

被引:37
作者
Abd Elaziz, Mohamed [1 ,2 ,3 ,4 ,5 ]
Abualigah, Laith [6 ,7 ]
Ibrahim, Rehab Ali [1 ]
Attiya, Ibrahim [1 ,2 ]
机构
[1] Zagazig Univ, Dept Math, Fac Sci, Zagazig 44519, Egypt
[2] Acad Sci Res & Technol ASRT, 101 Qasr Al Aini St,Cairo POB 11516, Cairo, Egypt
[3] Ajman Univ, Artificial Intelligence Res Ctr AIRC, Ajman 346, U Arab Emirates
[4] Galala Univ, Fac Comp Sci Engn, Suze 435611, Egypt
[5] Tomsk Polytech Univ, Sch Comp Sci & Robot, Tomsk 634050, Russia
[6] Amman Arab Univ, Fac Comp Sci & Informat, Amman 11953, Jordan
[7] Univ Sains Malaysia, Sch Comp Sci, Gelugor 11800, Pulau Pinang, Malaysia
关键词
CLOUD; ENVIRONMENT;
D O I
10.1155/2021/9114113
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:14
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