An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy
Abstract Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is a...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86814-1 |
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| author | Roqia Rateb Ahmed Adnan Hadi Venkata Mohit Tamanampudi Laith Abualigah Absalom E. Ezugwu Ahmed Ibrahim Alzahrani Fahad Alblehai Heming Jia |
| author_facet | Roqia Rateb Ahmed Adnan Hadi Venkata Mohit Tamanampudi Laith Abualigah Absalom E. Ezugwu Ahmed Ibrahim Alzahrani Fahad Alblehai Heming Jia |
| author_sort | Roqia Rateb |
| collection | DOAJ |
| description | Abstract Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability. Therefore, in order to achieve these objectives, the combination of Aquila and Salp Swarm Algorithms (ASSA) is used to select the best Virtual Machines (VMs) for the execution of workflows. So, in each iteration of ASSA execution, a number of VMs are selected by the ASSA. Then by using the Reducing MakeSpan Time (RMST) technique, the MST of the workflow on selected VMs is reduced, while maintaining reliability and deadline. Then, using VM merging and Dynamic Voltage Frequency Scaling (DVFS) technique on the output from RMST, the static and dynamic EC is reduced, respectively. Experimental results show the effectiveness of the proposed method compared to previous methods. |
| format | Article |
| id | doaj-art-fa6084724af14476bc5d55cb9012949c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fa6084724af14476bc5d55cb9012949c2025-02-02T12:18:35ZengNature PortfolioScientific Reports2045-23222025-01-0115113110.1038/s41598-025-86814-1An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energyRoqia Rateb0Ahmed Adnan Hadi1Venkata Mohit Tamanampudi2Laith Abualigah3Absalom E. Ezugwu4Ahmed Ibrahim Alzahrani5Fahad Alblehai6Heming Jia7Department of Computer Science, College of Information Technology, Al-Ahliyya Amman UniversityArtificial Intelligence Sciences Department, College of Sciences, Al-Mustaqbal UniversityJPMorgan ChaseComputer Science Department, Al Al-Bayt UniversityUnit for Data Science and Computing, North-West UniversityComputer Science Department, Community College, King Saud UniversityComputer Science Department, Community College, King Saud UniversitySchool of Information Engineering, Sanming UniversityAbstract Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability. Therefore, in order to achieve these objectives, the combination of Aquila and Salp Swarm Algorithms (ASSA) is used to select the best Virtual Machines (VMs) for the execution of workflows. So, in each iteration of ASSA execution, a number of VMs are selected by the ASSA. Then by using the Reducing MakeSpan Time (RMST) technique, the MST of the workflow on selected VMs is reduced, while maintaining reliability and deadline. Then, using VM merging and Dynamic Voltage Frequency Scaling (DVFS) technique on the output from RMST, the static and dynamic EC is reduced, respectively. Experimental results show the effectiveness of the proposed method compared to previous methods.https://doi.org/10.1038/s41598-025-86814-1Fog-cloud computingWorkflow schedulingDynamic Voltage Frequency ScalingAquila-Salp Swarm Algorithm |
| spellingShingle | Roqia Rateb Ahmed Adnan Hadi Venkata Mohit Tamanampudi Laith Abualigah Absalom E. Ezugwu Ahmed Ibrahim Alzahrani Fahad Alblehai Heming Jia An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy Scientific Reports Fog-cloud computing Workflow scheduling Dynamic Voltage Frequency Scaling Aquila-Salp Swarm Algorithm |
| title | An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy |
| title_full | An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy |
| title_fullStr | An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy |
| title_full_unstemmed | An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy |
| title_short | An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy |
| title_sort | optimal workflow scheduling in iot fog cloud system for minimizing time and energy |
| topic | Fog-cloud computing Workflow scheduling Dynamic Voltage Frequency Scaling Aquila-Salp Swarm Algorithm |
| url | https://doi.org/10.1038/s41598-025-86814-1 |
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