Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach
Abstract Due to its enormous influence on system functionality, researchers are presently looking into the issue of task scheduling on multiprocessors. Establishing the most advantageous schedules is often regarded as a difficult-to-compute issue. Genetic Algorithm is a recent tool employed by resea...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80910-4 |
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| author | Heba E. Hassan Khaled Hosny Ibrahiem Ahmed H. Madian |
| author_facet | Heba E. Hassan Khaled Hosny Ibrahiem Ahmed H. Madian |
| author_sort | Heba E. Hassan |
| collection | DOAJ |
| description | Abstract Due to its enormous influence on system functionality, researchers are presently looking into the issue of task scheduling on multiprocessors. Establishing the most advantageous schedules is often regarded as a difficult-to-compute issue. Genetic Algorithm is a recent tool employed by researchers to optimize scheduling tasks and boost performance, although this field of research is yet mostly unexplored. In this article, a novel approach for generating task schedules for real-time systems utilizing a Genetic Algorithm is proposed. The approach seeks to design task schedules for multiprocessor systems with optimal or suboptimal lengths, with the ultimate goal of achieving high performance. This research project focuses on non-preemptive independent tasks in a multiprocessor environment. All processors are assumed to be identical. We conducted a thorough analysis of the proposed approach and pitted it against three frequently utilized scheduling methodologies: the “Evolutionary Fuzzy Based Scheduling Algorithm”, the “Least Laxity First Algorithm”, and the “Earliest Deadline First Algorithm”. The Proposed Algorithm demonstrated superior efficiency and reliability compared to Earliest Deadline First, Least Laxity First, and Evolutionary Fuzzy-based Scheduling Algorithm. It consistently achieved zero missed deadlines and the lowest average response and turnaround times across all scenarios, maintaining optimal performance even under high load conditions. |
| format | Article |
| id | doaj-art-2a72e466e89a4d358659c401cb88847a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2a72e466e89a4d358659c401cb88847a2025-02-02T12:24:29ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-80910-4Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approachHeba E. Hassan0Khaled Hosny Ibrahiem1Ahmed H. Madian2Department of Electrical Engineering, Faculty of Engineering, Fayoum UniversityDepartment of Electrical Engineering, Faculty of Engineering, Fayoum UniversityNanoelectronics Integrated Systems Center (NISC), Nile UniversityAbstract Due to its enormous influence on system functionality, researchers are presently looking into the issue of task scheduling on multiprocessors. Establishing the most advantageous schedules is often regarded as a difficult-to-compute issue. Genetic Algorithm is a recent tool employed by researchers to optimize scheduling tasks and boost performance, although this field of research is yet mostly unexplored. In this article, a novel approach for generating task schedules for real-time systems utilizing a Genetic Algorithm is proposed. The approach seeks to design task schedules for multiprocessor systems with optimal or suboptimal lengths, with the ultimate goal of achieving high performance. This research project focuses on non-preemptive independent tasks in a multiprocessor environment. All processors are assumed to be identical. We conducted a thorough analysis of the proposed approach and pitted it against three frequently utilized scheduling methodologies: the “Evolutionary Fuzzy Based Scheduling Algorithm”, the “Least Laxity First Algorithm”, and the “Earliest Deadline First Algorithm”. The Proposed Algorithm demonstrated superior efficiency and reliability compared to Earliest Deadline First, Least Laxity First, and Evolutionary Fuzzy-based Scheduling Algorithm. It consistently achieved zero missed deadlines and the lowest average response and turnaround times across all scenarios, maintaining optimal performance even under high load conditions.https://doi.org/10.1038/s41598-024-80910-4MultiprocessorsTask SchedulingGenetic algorithmsPerformance utilizationMultiprocessorNo-Preemptions |
| spellingShingle | Heba E. Hassan Khaled Hosny Ibrahiem Ahmed H. Madian Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach Scientific Reports Multiprocessors Task Scheduling Genetic algorithms Performance utilization Multiprocessor No-Preemptions |
| title | Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach |
| title_full | Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach |
| title_fullStr | Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach |
| title_full_unstemmed | Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach |
| title_short | Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach |
| title_sort | optimizing multiprocessor performance in real time systems using an innovative genetic algorithm approach |
| topic | Multiprocessors Task Scheduling Genetic algorithms Performance utilization Multiprocessor No-Preemptions |
| url | https://doi.org/10.1038/s41598-024-80910-4 |
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