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Dynamic DVFS Scheduling

  • Padmanabhan S. Pillai
  • Kang G. Shin

As discussed in the previous chapter, offline analysis can be used to generate a schedule of DVFS state changes to minimize energy consumption, while ensuring sufficient processing cycles are available for all tasks to meet their deadlines, even under worst-case computation requirements. However, invocations of real-time tasks typically use less than their specified worst-case computation requirements, presenting an opportunity for further energy conservation. This chapter outlines three online, dynamic techniques to more aggressively scale back processing frequency and voltage to conserve energy when task computation cycles vary, yet continue to provide timeliness guarantees for worst-case execution time scenarios.

Keywords

Schedulability Test Dynamic Voltage Scaling Actual Execution Time Task Utilization Rate Monotonic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    AYDIN, H., MELHEM, R., MOSSE, D., AND MEJIA -ALVAREZ, P. Power-aware scheduling for periodic real-time tasks. IEEE Transactions on Computing 53, 5 (2004), 584-600.CrossRefGoogle Scholar
  2. [2]
    DUDANI, A., MUELLER, F., AND ZHU, Y. Energy-conserving feed-back EDF scheduling for embedded systems with real-time constraints. In ACM SIGPLAN Joint Conference Languages, Compilers, and Tools for Embedded Systems (LCTES’02) and Software and Compilers for Embed-ded Systems (SCOPES’02) (June 2002), pp. 213-222.Google Scholar
  3. [3]
    GRUIAN, F. Hard real-time scheduling for low energy using stochastic data and DVS processors. In Proceedings of the International Symposium on Low-Power Electronics and Design ISLPED’01 (Huntington Beach, CA, Aug. 2001).Google Scholar
  4. [4]
    KIM, W., SHIN, D., YUN, H. -S., KIM, J., AND MIN, S.L. Perfor-mance comparison of dynamic voltage scaling algorithms for hard real-time systems. In Proceedings of the 8th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’02) (2002).Google Scholar
  5. [5]
    KRISHNA, C.M., AND LEE, Y. -H. Voltage-clock-scaling techniques for low power in hard real-time systems. In Proceedings of the IEEE Real-Time Technology and Applications Symposium (Washington, DC, May 2000), pp. 156-165.Google Scholar
  6. [6]
    KRISHNA, C.M., AND SHIN, K.G. Real-Time Systems. McGraw-Hill, 1997.Google Scholar
  7. [7]
    LEHOCZKY, J., SHA, L., AND DING, Y. The rate monotonic scheduling algorithm: exact characterization and average case behavior. In Proceed-ings of the IEEE Real-Time Systems Symposium (1989), pp. 166-171.Google Scholar
  8. [8]
    LEHOCZKY, J., AND THUEL, S. Algorithms for scheduling hard aperi-odic tasks in fixed-priority systems using slack stealing. In Proceedings of the IEEE Real-Time Systems Symposium (1994).Google Scholar
  9. [9]
    LEHOCZKY, J.P., SHA, L., AND STROSNIDER, J.K. Enhanced aperiodic responsiveness in hard real-time environments. In Proceedings of the 8th IEEE Real-Time Systems Symposium (Los Alamitos, CA, Dec. 1987), pp. 261-270.Google Scholar
  10. [10]
    LEUNG, J. Y. -T., AND WHITEHEAD, J. On the complexity of fixed-priority scheduling of periodic, real-time tasks. Performance Evaluation 2,4(1982),237-250.MATHCrossRefMathSciNetGoogle Scholar
  11. [11]
    LIU, C.L., AND LAYLAND, J.W. Scheduling algorithms for multipro-gramming in a hard real-time environment. Journal of the ACM 20, 1 (1973),46-61.MATHCrossRefMathSciNetGoogle Scholar
  12. [12]
    LU, Z., HEIN, J., HUMPHREY, M., STAN, M., LACH, J., AND SKADRON, K. Control-theoretic dynamic frequency and voltage scaling for multimedia workloads. In CASES ’02: Proceedings of the 2002 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (2002), pp. 156-163.Google Scholar
  13. [13]
    PILLAI, P., AND SHIN, K. G. Real-time dynamic voltage scaling for low-power embedded operating systems. In Proceedings of the 18th ACM Symposium on Operating Systems Principles (Banff, Alberta, CA, Oct. 2001), pp. 89-102.Google Scholar
  14. [14]
    STANKOVIC, J., ET AL. Deadline Scheduling for Real-Time Systems. Kluwer Academic Publishers, 1998.Google Scholar
  15. [15]
    SWAMINATHAN, V., AND CHAKRABARTY, K. Real-time task schedul-ing for energy-aware embedded systems. In Proceedings of the IEEE Real-Time Systems Symp. (Work-in-Progress Session) (Orlando, FL, Nov. 2000).Google Scholar
  16. [16]
    VARMA, A., GANESH, B., SEN, M., CHOUDHARY, S. R.,SRINIVASAN, L., AND JACOB, B. A control-theoretic approach to dynamic voltage scaling. In Proceedings of International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES 2003) (Oct. 2003), pp. 255-266.Google Scholar
  17. [17]
    ZHU, Y., AND MUELLER, F. Feedback EDF scheduling exploiting dynamic voltage scaling. In 10th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’04) (2004).Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Padmanabhan S. Pillai
    • 1
  • Kang G. Shin
    • 2
  1. 1.Intel Research PittsburghPittsburghUSA
  2. 2.Electrical Engineering and Computer Science DepartmentUniversity of MichiganAnn ArborUSA

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