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Energy efficient scheduler of aperiodic jobs for real-time embedded systems

  • Hussein El GhorEmail author
  • E. M. Aggoune
Research Article

Abstract

Energy consumption has become a key metric for evaluating how good an embedded system is, alongside more performance metrics like respecting operation deadlines and speed of execution. Schedulability improvement is no longer the only metric by which optimality is judged. In fact, energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system’s lifetime. In this work, we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption. Specifically, we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling (DVFS) technique. We address a variant of earliest deadline first (EDF) scheduling algorithm called energy saving-dynamic voltage and frequency scaling (ES-DVFS) algorithm that is suited to unpredictable future energy production and irregular job arrivals. We prove that ES-DVFS cannot attain a total value greater than C/000000, where 000000 is the minimum speed of any job and C is the available energy capacity. We also investigate the implications of having in advance, information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS. We show that such advance knowledge makes possible the design of semi-on-line algorithm, ES-DVFS**, that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor. The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.

Keywords

Real-time systems energy efficiency aperiodic jobs scheduling dynamic voltage scaling low-power systems embedded systems 

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.University Institute of Technology, Lebanese UniversityLebanon Sensor Networks and Cellular Systems (SNCS) Research Center UTTabukSaudi Arabia
  2. 2.Electrical Engineering DepartmentUniversity of Tabuk Sensor Networks and Cellular Systems (SNCS) Research CenterTabukSaudi Arabia

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