An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing
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 applica...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/7/1100 |
| _version_ | 1797438546243485696 |
|---|---|
| author | Ibrahim Attiya Laith Abualigah Doaa Elsadek Samia Allaoua Chelloug Mohamed Abd Elaziz |
| author_facet | Ibrahim Attiya Laith Abualigah Doaa Elsadek Samia Allaoua Chelloug Mohamed Abd Elaziz |
| author_sort | Ibrahim Attiya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:38:30Z |
| format | Article |
| id | doaj.art-b6681ebc4b9e4bb2adbc747a92f6c1d6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:38:30Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b6681ebc4b9e4bb2adbc747a92f6c1d62023-11-30T23:37:11ZengMDPI AGMathematics2227-73902022-03-01107110010.3390/math10071100An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog ComputingIbrahim Attiya0Laith Abualigah1Doaa Elsadek2Samia Allaoua Chelloug3Mohamed Abd Elaziz4Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptFaculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, JordanDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Computer Science & Engineering, Galala University, Suze 435611, EgyptThe 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.https://www.mdpi.com/2227-7390/10/7/1100chimp optimization algorithmmarine predators algorithmcloud computingfog computingtask schedulingmakespan |
| spellingShingle | Ibrahim Attiya Laith Abualigah Doaa Elsadek Samia Allaoua Chelloug Mohamed Abd Elaziz An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing Mathematics chimp optimization algorithm marine predators algorithm cloud computing fog computing task scheduling makespan |
| title | An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing |
| title_full | An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing |
| title_fullStr | An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing |
| title_full_unstemmed | An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing |
| title_short | An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing |
| title_sort | intelligent chimp optimizer for scheduling of iot application tasks in fog computing |
| topic | chimp optimization algorithm marine predators algorithm cloud computing fog computing task scheduling makespan |
| url | https://www.mdpi.com/2227-7390/10/7/1100 |
| work_keys_str_mv | AT ibrahimattiya anintelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT laithabualigah anintelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT doaaelsadek anintelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT samiaallaouachelloug anintelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT mohamedabdelaziz anintelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT ibrahimattiya intelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT laithabualigah intelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT doaaelsadek intelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT samiaallaouachelloug intelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing AT mohamedabdelaziz intelligentchimpoptimizerforschedulingofiotapplicationtasksinfogcomputing |