IDEAS home Printed from https://ideas.repec.org/a/ajp/edwast/v9y2025i5p607-614id6956.html
   My bibliography  Save this article

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

Author

Listed:
  • Noor K. Younis
  • Marwa Riyadh Ahmed
  • Azhar W. Talab
  • Rabei Raad Ali

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.

Suggested Citation

  • Noor K. Younis & Marwa Riyadh Ahmed & Azhar W. Talab & Rabei Raad Ali, 2025. "Testing real-time system algorithms performance on synthetic Data: Analytical study of hard and soft system tasks," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(5), pages 607-614.
    • Handle: RePEc:ajp:edwast:v:9:y:2025:i:5:p:607-614:id:6956
    as

    Download full text from publisher

    File URL: https://learning-gate.com/index.php/2576-8484/article/view/6956/2434
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ajp:edwast:v:9:y:2025:i:5:p:607-614:id:6956. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Melissa Fernandes (email available below). General contact details of provider: https://learning-gate.com/index.php/2576-8484/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.