Exploiting Dynamic Workload Variation in Offline Low Energy Voltage Scheduling

  • Lap-Fai Leung
  • Chi-Ying Tsui
  • Xiaobo Sharon Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3254)


In this paper, a novel off-line voltage scheduling algorithm, which exploit the dynamic workload variation is proposed. During the construction of the voltage schedule, instead of optimizing the energy consumption assuming all the tasks are running in the worst case workload, we derive a schedule that results in low energy consumption when the tasks are running at a given workload distribution while at the same time can guarantee no deadline violation when the worst-case scenario really happens. By doing so, more slacks are generated and lower voltages can be used when the tasks are really running at workloads that are less than the worst case values. This work can be viewed as an interaction between the off-line voltage scheduling and on-line dynamic voltage scaling. The problem is formulated as a constrained optimization problem and optimal solution is obtained. Simulation and trace-based results show that, by using the proposed scheme, significant energy reduction is obtained for both randomly generated task sets and real-life applications when comparing with the existing best off-line voltage scheduling approach.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Burd, T., Pering, T., Stratakos, A., Brodersen, R.: A dynamic voltage scaled microprocessor system. IEEE Journal of Solid-State Circuits 35, 1571–1580 (2000)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    AMD Athlon 4 Processor, Data Sheet Reference #24319, AMD, Inc. (2001) Google Scholar
  4. 4.
    Hong, Kirovski, D., Qu, G., Potkonjak, M., Srivastava, M.: Power optimization of variable voltage core-based systems. In: DAC, pp. 176–181 (1998)Google Scholar
  5. 5.
    Ishihara, T., Yasuura, H.: Voltage Scheduling Problem for Dynamically Variable Voltage Processors. In: Proceedings ISLPED, pp. 197–202 (1998)Google Scholar
  6. 6.
    Kim, W., Kim, J., Min, S.L.: A Dynamic Voltage Scaling Algorithm for Dynamic-Priority Hard Real-Time Systems Using Slack Time Analysis. In: DATE (2002)Google Scholar
  7. 7.
    Pillai, P., Shin, K.: Real-time dynamic voltage scaling for low-power embedded operating systems. In: ACM Symposium on Operating Systems and Principles (2001)Google Scholar
  8. 8.
    Sinha, A., Chandrakasan, A.: Dynamic Voltage Scheduling Using Adaptive Filtering of Workload Traces. In: 14th Intl. conference on VLSI Design, Bangalore, India (January 2001)Google Scholar
  9. 9.
    Zhu, D., Melhem, R., Childers, B.R.: Scheduling with dynamic voltage/ speed adjustment using slack reclamation in multiprocessor real-time systems. IEEE Trans. On Prarallel and Distributed Systems 14(7) (July 2003)Google Scholar
  10. 10.
    Gruian, F.: Hard Real-Time Scheduling for Low-Energy Using Stochastic Data and DVS Processors. In: The Proceedings of ISLPED, August 2001, pp. 46–51 (2001)Google Scholar
  11. 11.
    Zhang, Y., Hu, X., Chen, D.Z.: Task Scheduling and Voltage Selection for Energy Minimization. In: DAC, pp. 183–188 (2002)Google Scholar
  12. 12.
    Saewong, S., Rajkumar, R.: Practical voltage-scaling for fixed-priority rt-systems. In: Proceedings of the 9th RTAS, pp. 106–114 (2003)Google Scholar
  13. 13.
    Mok, Y.L.A.K.: An integrated approach for applying dynamic voltage scaling to hard real-time systems. In: Proceedings of the 9th RTAS, pp. 116–123 (2003)Google Scholar
  14. 14.
    Li, Y.-T.S., Malik, S., Wolfe, A.: Performance estimation of embedded software with instruction cache modeling. Design Automation Electron. Syst. 4(3) (1999)Google Scholar
  15. 15.
    Gruian, F., Kuchcinski, K.: Uncertainty-Based Scheduling: Energy-Efficient Ordering for Tasks with Variable Execution Time. In: ISLPED, pp. 465–468 (2003)Google Scholar
  16. 16.
    Mochocki, B., Hu, X.S., Quan, G.: A realistic vaiable voltage scheduling model for real-time applications. In: ICCAD, pp. 726–731 (2002)Google Scholar
  17. 17.
    Hochbaum, D., Shanthikumar, J.: Convex separable optimization is not much harder than linear optimization. Journal of ACM 37(4), 843–862 (1990)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Kim, N., Ryu, M., Hong, S., Saksena, M., Choi, C.-H., Shin, H.: Visual assessment of a real-time system design: a case study on a CNC controller. In: The 17th IEEE Real-Time Systems Symposium, pp. 300–310 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Lap-Fai Leung
    • 1
  • Chi-Ying Tsui
    • 1
  • Xiaobo Sharon Hu
    • 2
  1. 1.Department of Electrical and Electronic EngineeringHong Kong University of Science and TechnologyHong Kong SARChina
  2. 2.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

Personalised recommendations