Energy Guarantee Scheme for Real-time Systems with Energy Harvesting Constraints

  • Hussein El GhorEmail author
  • Maryline Chetto
Research Article


The growth of environmental energy harvesting has been explosive in wireless computing systems especially when replacing or recharging batteries manually is impracticable. This work investigates the scheduling of periodic weekly hard real-time tasks under energy constraints. Based on this motivation, we proposed a real-time scheduling algorithm, namely energy guarantee dynamic voltage and frequency scaling (EG-DVFS), that utilizes the earliest deadline-harvesting (ED-H) scheduling algorithm combined with dynamic voltage and frequency scaling. This one is qualified as real-time since tasks must satisfy their timing constraints. We assume that the preemptable tasks receive dynamic priorities according to the earliest deadline first (EDF) rule. EG-DVFS adjusts the processor′s behavior by characterizing the properties of the energy source module, capacity of the stored energy as well as the harvested energy in a future duration. Specifically, tasks are executed at full processor speed if the amount of energy in the battery is enough to finish its execution. Otherwise, the processor slows down task execution to the lowest possible processor speed while still guaranteeing to meet all the timing constraints. EG-DVFS mainly depends on the on-line computation of the slack time and the slack energy with dynamic voltage and frequency selection in order to achieve an improved system performance. Experimental results show that EG-DVFS can achieve capacity savings up of up to 33% when compared to ED-H.


Real-time systems energy harvesting embedded systems power management dynamic voltage and frequency selection (DVFS) ED-H scheduler 


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  1. [1]
    R. Mishra, N. Rastogi, D. K. Zhu, D. Mosse, R. Melhem. Energy aware scheduling for distributed real-time systems. In Proceedings of International Parallel and Distributed Processing Symposium, Nice, France, pp. 21–29, 2003. Doi: 10.1109/IPDPS.2003.1213099.Google Scholar
  2. [2]
    Q. R. Qiu, S. B. Liu, Q. Wu. Task merging for dynamic power management of cyclic applications in real-time multiprocessor systems. In Proceedings of International Conference on Computer Design, San Jose, USA, pp. 397–404, 2006. Doi: 10.1109/ICCD.2006.4380847.Google Scholar
  3. [3]
    I. Hong, D. Kirovski, G. Qu, M. Potkonjak, M. B. Srivastava. Power optimization of variable-voltage core-based systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 18, no. 1, pp. 1702–1714, 1999. Doi: 10.1109/43.811318.CrossRefGoogle Scholar
  4. [4]
    D. J. Cook, S. K. Das. Smart Environments: Technologies, Protocols, and Applications, New York, USA: John Wiley, 2004.CrossRefGoogle Scholar
  5. [5]
    R. Nallusamy, K. Duraiswamy. Solar powered wireless sensor networks for environmental applications with energy efficient routing concepts: A review. Information Technology Journal, vol. 10, pp. 1–10, 2011. Doi: 10.3923/itj.2011.1.10.CrossRefGoogle Scholar
  6. [6]
    S. Roundy, D. Steingart, L. Frechette, P. Wright, J. Rabaey. Power sources for wireless sensor networks. In Proceedings of the 1st European Workshop Wireless Sensor Networks, Springer, Berlin, Germany, pp. 1–17, 2004. Doi: 10.1007/978-3-540-24606-0—1.Google Scholar
  7. [7]
    R. Kotz, M. Carlen. Principles and applications of electrochemical capacitors. Electrochimica Acta, vol. 45, no. 15–16, pp. 2483–2498, 2000. Doi: 10.1016/S0013-4686(00) 00354.CrossRefGoogle Scholar
  8. [8]
    V. Raghunathan, A. Kansal, J. Hsu, J. Friedman, M. Srivastava. Design considerations for solar energy harvesting wireless embedded systems. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Boise, USA, pp. 457–462, 2005. Doi: 10.1109/IPSN.2005.1440973.Google Scholar
  9. [9]
    X. Jiang, J. Polastre, D. Culler. Perpetual environmentally powered sensor networks. In Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Boise, USA, pp. 463–468, 2005. Doi: 10.1109/IPSN.2005.1440974.Google Scholar
  10. [10]
    A. Kansal, J. Hsu, S. Zahedi, M. B. Srivastava. Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems, vol. 6, no. 4, Article number 32, 2007. Doi: 10.1145/1274858. 1274870.Google Scholar
  11. [11]
    A. Kansal, J. Hsu, M. Srivastava, V. Raqhunathan. Harvesting aware power management for sensor networks. In IEEE Proceedings of the 43rd ACM/IEEE Design Automation Conference, San Francisco, USA, 2006. DOI: 10.1145/1146909.1147075.Google Scholar
  12. [12]
    J. Hsu, S. Zahedi, A. Kansal, M. Srivastava, V. Raghunathan. Adaptive duty cycling for energy harvesting systems. In Proceedings of International Symposium on Low Power Electronics and Design, Tegernsee, Germany, pp. 180–185, 2006. DOI: 10.1145/1165573.1165616.Google Scholar
  13. [13]
    C. Moser, D. Brunelli, L. Thiele, L. Benini. Real-time scheduling for energy harvesting sensor nodes. Real-Time Systems, vol. 37, no. 3, pp. 233–260, 2007. DOI: 10.1007/s11241-007-9027-0.CrossRefzbMATHGoogle Scholar
  14. [14]
    C. L. Liu, J. W. Layland. Scheduling algorithms for multi-programming in a hard-real-time environment. Journal of the ACM, vol. 20, no. 1, pp. 46–61, 1973. DOI: 10.1145/321738.321743.CrossRefzbMATHGoogle Scholar
  15. [15]
    R. Jayaseelan, T. Mitra, X. F. Li. Estimating the worstcase energy consumption of embedded software. In Proceedings of the 12th IEEE Real-time and Embedded Technology and Applications Symposium, San Jose, USA, pp. 81–90, 2006. DOI: 10.1109/RTAS.2006.17.Google Scholar
  16. [16]
    M. Chetto. Optimal scheduling for real-time jobs in energy harvesting computing systems. IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 2, pp. 122–133, 2014. DOI: 10.1109/TETC.2013.2296537.CrossRefGoogle Scholar
  17. [17]
    H. El Ghor, M. Chetto, R. Hage Chehade. EH-EDF: An on-line scheduler for real-time energy harvesting systems. In Proceedings of the 18th IEEE International Conference on Electronics, Circuits, and Systems, Beirut, Lebanon, pp. 776–779, 2011. DOI: 10.1109/ICECS.2011.6122389.Google Scholar
  18. [18]
    A. Allavena, D. Mosse. Scheduling of frame-based embedded systems with rechargeable batteries. In Proceedings of Workshop on Power Management for Real-time and Embedded Systems, Taipei, China, 2001.Google Scholar
  19. [19]
    H. El Ghor, E. M. Aggoune. Energy efficient scheduler of aperiodic jobs for real-time embedded systems. International Journal of Automation and Computing, 2016, published online. DOI: 10.1007/s11633-016-0993-3.Google Scholar
  20. [20]
    S. B. Liu, Q. Qiu, Q. Wu. Energy aware dynamic voltage and frequency selection for real-time systems with energy harvesting. In Proceedings of Design, Automation and Test in EUROPE, Munich, Germany, pp. 236–241, 2008. DOI: 10.1109/DATE.2008.4484692.Google Scholar
  21. [21]
    S. B. Liu, J. Lu, Q. Wu, Q. R. Qiu. Harvesting-aware power management for real-time systems with renewable energy. IEEE Transactions on Very Large Scale Integration Systems, vol. 20, no. 8, pp. 1473–1486, 2012. DOI: 10.1109/TVLSI.2011.2159820.Google Scholar
  22. [22]
    X. Lin, Y. Z. Wang, S. Y. Yue, N. Chang, M. Pedram. A framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting. In Proceedings of International Symposium on Low Power Electronics and Design, Beijing, China, pp. 70–75, 2013. DOI: 10.1109/ISLPED. 2013.6629269.Google Scholar
  23. [23]
    B. Srbinovski, M. Magno, B. O'Flynn, V. Pakrashi, E. Popovici. Energy aware adaptive sampling algorithm for energy harvesting wireless sensor networks. In Proceedings of IEEE Sensors Applications Symposium, Zadar, Croatia, 2015. DOI: 10.1109/SAS.2015.7133582.Google Scholar
  24. [24]
    Y. H. Tan, X. D. Yin. A dynamic scheduling algorithm for energy harvesting embedded systems. EURASIP Journal on Wireless Communications and Networking, vol. 2016, Article number 114, 2016. DOI: 10.1186/s13638-016-0602-8.Google Scholar
  25. [25]
    H. Z. Xu, R. F. Li, L. N. Zeng, K. Q. Li, C. Pand. Energy-efficient scheduling with reliability guarantee in embedded real-time systems. Sustainable Computing, Informatics and Systems, vol. 18, pp. 137–148, 2018. DOI: 10. 1016/j.suscom.2018.01.005.CrossRefGoogle Scholar
  26. [26]
    M. L. Dertouzos. Control robotics: The procedural control of physical processes. In Proceedings of International Federation for Information Processing, Stockholm, Sweden, pp. 807–813, 1974.Google Scholar
  27. [27]
    J. W. S. Liu. Real-time Systems, New Jersey, USA: Prentice-Hall, 2000.Google Scholar
  28. [28]
    H. Chetto, M. Chetto. Some results of the earliest deadline scheduling algorithm. IEEE Transactions on Software Engineering, vol. 15, no. 10, pp. 1261–1269, 1989. DOI: 10.1109/TSE.1989.559777.MathSciNetCrossRefzbMATHGoogle Scholar
  29. [29]
    M. Silly. The EDL server for scheduling periodic and soft aperiodic tasks with resource constraints. Real-time Systems, vol. 17, no. 1, pp. 87–111, 1999. DOI: 10.1023/A:1008093629946.CrossRefGoogle Scholar
  30. [30]
    M. Chetto, A. Queudet. Clairvoyance and online scheduling in real-time energy harvesting systems. Real-time Systems, vol. 50, no. 2, pp. 179–184, 2014. DOI: 10.1007/s11241-013-9193-1.CrossRefzbMATHGoogle Scholar
  31. [31]
    J. Y. T. Leung, J. Whitehead. On the complexity of fixedpriority scheduling of periodic, real-time tasks. Performance Evaluation, vol. 2, no. 4, pp. 237–250, 1982. DOI: 10.1016/0166-5316(82)90024-4.MathSciNetCrossRefzbMATHGoogle Scholar
  32. [32]
    P. Martineau. Online Scheduling In Real-Time Systems, Ph. D. dissertation, University of Nantes, France, 1994.Google Scholar
  33. [33]
    Intel Corp. Intel XScale Processor Family Electrical, Mechanical, and Thermal Specification Datasheet, Technical Report, Santa Clara, USA, 2004.Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Embedded and Networked Systems, Faculty of TechnologyLebanese UniversitySaidaLebanon
  2. 2.Laboratory of Numerical Sciences of NantesUniversity of NantesCarquefouFrance

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