Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments
Abstract Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and compar...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-75859-3 |
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| author | Deafallah Alsadie Musleh Alsulami |
| author_facet | Deafallah Alsadie Musleh Alsulami |
| author_sort | Deafallah Alsadie |
| collection | DOAJ |
| description | Abstract Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios. |
| format | Article |
| id | doaj-art-4dd5ed046ebd4d69a36277c63f23ffce |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4dd5ed046ebd4d69a36277c63f23ffce2025-08-20T02:11:17ZengNature PortfolioScientific Reports2045-23222024-10-0114111910.1038/s41598-024-75859-3Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environmentsDeafallah Alsadie0Musleh Alsulami1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura UniversityDepartment of Software Engineering, College of Computing, Umm Al-Qura UniversityAbstract Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.https://doi.org/10.1038/s41598-024-75859-3Scientific workflowsCloud computingScheduling algorithmsFirefly Optimization AlgorithmResource utilization |
| spellingShingle | Deafallah Alsadie Musleh Alsulami Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments Scientific Reports Scientific workflows Cloud computing Scheduling algorithms Firefly Optimization Algorithm Resource utilization |
| title | Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments |
| title_full | Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments |
| title_fullStr | Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments |
| title_full_unstemmed | Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments |
| title_short | Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments |
| title_sort | enhancing workflow efficiency with a modified firefly algorithm for hybrid cloud edge environments |
| topic | Scientific workflows Cloud computing Scheduling algorithms Firefly Optimization Algorithm Resource utilization |
| url | https://doi.org/10.1038/s41598-024-75859-3 |
| work_keys_str_mv | AT deafallahalsadie enhancingworkflowefficiencywithamodifiedfireflyalgorithmforhybridcloudedgeenvironments AT muslehalsulami enhancingworkflowefficiencywithamodifiedfireflyalgorithmforhybridcloudedgeenvironments |