Testing real-time system algorithms performance on synthetic Data: Analytical study of hard and soft system tasks

Noor K. Younis (), Marwa Riyadh Ahmed (), Azhar W. Talab () and Rabei Raad Ali ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 5, 607-614

Abstract: This study compares RMS, EDF, and LLF on synthetic datasets for hard and soft real-time systems to assess their feasibility and effectiveness in supporting real-time systems for various utilization levels. It has been designed to give each algorithm's various strengths, limits, and applicability in real-time application scenarios through total utilization computation and schedule-up-to-do ability analysis. It has been concluded that RMS is not schedulable due to its overutilization, while EDF is infeasible at Total > 1.0 for hard and soft real-time. LLF has limitations due to overutilization and frequent preemptions, making it suitable for soft real-time systems, unlike hard systems, because of the limitations of overutilization and frequent preemptions. RMS and EDF cannot meet deadlines under hard and soft real-time conditions. Future work should focus on hybrid algorithms or load balancing to overcome these limitations and process data in real time without tasks going beyond the time available to them in the CPU.

Keywords: Early deadline first; Feasibility; Utilization; Least Laxity First; Rate Monotonic Scheduling; Real time system; Synthetic datasets. (search for similar items in EconPapers)
Date: 2025
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