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

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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.

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Authors and Affiliations

  1. University Institute of Technology, Lebanese University, Lebanon Sensor Networks and Cellular Systems (SNCS) Research Center UT, Tabuk, Saudi Arabia

    Hussein El Ghor

  2. Electrical Engineering Department, University of Tabuk Sensor Networks and Cellular Systems (SNCS) Research Center, 71491, Tabuk, Saudi Arabia

    El-Hadi M. Aggoune

Authors
  1. Hussein El Ghor
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  2. El-Hadi M. Aggoune
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Correspondence to Hussein El Ghor.

Additional information

Recommended by Associate Editor Jinhua She

Hussein El Ghor received the engineering degree from the Lebanese University, Lebanon in 2002. He also received the Ph.D. degree in automatics and applied informatics from the University of Nantes, France in 2012. He is currently a member of the Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk, Saudi Arabia. He authored many papers in prestigious journals and conferences. His research interests include real-time scheduling and partitioning with particular emphasis on energy efficiency and energy harvesting systems.

El-Hadi M. Aggoune received his M. Sc. and Ph.D. degrees in electrical engineering from the University of Washington, USA. He is a professional engineer registered in the State of Washington, and senior member of the Institute of the IEEE. He has taught graduate and undergraduate courses in electrical engineering at a number of universities in the US and abroad. He served at many academic ranks including endowed chair professor and vice president and provost. He was the winner of the Boeing Supplier Excellence Award. He was also the winner of the IEEE professor of the Year Award, UW Branch. He is listed as inventor in a major patent assigned to the Boeing Company. His research work is referred to in many patents including patents assigned to ABB, Switzerland and EPRI, USA. Currently he is a professor and director of the Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk, Saudi Arabia. He authored many papers in IEEE and other journals and conferences. He is serving on many technical committees. His research interests include modeling and simulation of large scale networks, sensors and sensor networks, scientific visualization, and control and energy systems.

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El Ghor, H., Aggoune, EH.M. Energy efficient scheduler of aperiodic jobs for real-time embedded systems. Int. J. Autom. Comput. 17, 733–743 (2020). https://doi.org/10.1007/s11633-016-0993-3

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  • DOI: https://doi.org/10.1007/s11633-016-0993-3

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