Abstract
With the rapid development of embedded systems, battery life becomes a critical restriction factor. Dynamic voltage scaling (DVS) has been proven to be an effective method for reducing energy consumption of processors. This paper proposes an energy-saving algorithm under a task model (the MSPR model) where a task consists of multiple subtasks with different fixed priorities. This algorithm includes two parts. The first part is a static algorithm, which exploits the relationship among tasks to set the slowdown factors of subtasks. The second part is an algorithm that dynamically reclaims and reuses the slack time of precedent subtasks during the execution of tasks. To the best of our knowledge, this is the first work for energy-efficient scheduling under the complex periodic real-time task model where a task consists of multiple subtasks with different fixed priorities. Experimental results show this method can reduce energy consumption by 20%-80%, while guaranteeing the real-time requirements of systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Harbour, M., Klein, M.H., Lehoczky, J.: Timing analysis for fixed-priority scheduling of hard real-time systems. IEEE Trans. Software Eng. 20(2), 13–28 (1994)
Yao, F., Demers, A.J., Shenker, S.: A scheduling model for reduced CPU energy. In: Proc. IEEE Symp. Foundations Computer Science, pp. 374–382 (1995)
Saewong, S., Rajkumar, R.: Practical Voltage-Scaling for Fixed-Priority RT-Systems. In: Proc. Ninth IEEE Real-Time and Embedded Technology and Applications Symp, RTAS (2003)
Pillai, P., Shin, K.G.: Real-Time Dynamic Voltage Scaling for Low-Power Embedded Operating Systems. In: Proc. 18th ACM Symp. Operating Systems Principles (2001)
Gruian, F.: Hard real-time scheduling using stochastic data and DVS processors. In: International Symposium on Low Power Electronic and Design (2001)
Liu, Y., Mok, A.K.: An Integrated Approach for Applying Dynamic Voltage Scaling to Hard Real-Time Systems. In: Proc. Ninth IEEE Real-Time and Embedded Technology and Applications Symp., pp. 116–123 (2003)
Aydin, H., Melhem, R., Mossé, D., Mejía-Alvarez, P.: Power-Aware Scheduling for Periodic Real-Time Tasks. IEEE Trans. Computers. 53(5), 584–600 (2004)
Qadi, A., Goddard, S., Farritor, S.: A Dynamic Voltage Scaling Algorithm for Sporadic Tasks. In: Proc. 24th Real-Time Systems Symp., pp. 52–62 (2003)
Lehoczky, J., Sha, L., Ding, Y.: The rate monotonic scheduling algorithm: exact characterization and average case behavior. In: Proc. IEEE Real-Time Systems Symposium, pp. 166–171 (1989)
Gruian, F., Kuchcinski, K.: LEneS: task scheduling for low-energy systems using variable voltage processors. In: Proc. 2001 conference on Asia South Pacific design automation (ASP-DAC), pp. 449–455 (2001)
Zhu, D., Mossé, D., Melhem, R.G.: Power-Aware Scheduling for AND/OR Graphs in Real-Time Systems. IEEE Trans. Parallel Distrib. Syst. 15(9), 849–864 (2004)
Yun, H., Kim, J.: On energy-optimal voltage scheduling for fixed priority hard real-time systems. Trans. Embed. Comput. Syst. 2(3), 393–430 (2003)
Dertouzos, M.L., Mok, A.K.: Multiprocessor On-Line Scheduling of Hard-Real-Time Tasks. IEEE Trans. Software Eng. 15(12), 1497–1505 (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Gao, Z., Wu, Z., Lin, M. (2007). Energy-Efficient Fixed-Priority Scheduling for Periodic Real-Time Tasks with Multi-priority Subtasks. In: Lee, YH., Kim, HN., Kim, J., Park, Y., Yang, L.T., Kim, S.W. (eds) Embedded Software and Systems. ICESS 2007. Lecture Notes in Computer Science, vol 4523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72685-2_53
Download citation
DOI: https://doi.org/10.1007/978-3-540-72685-2_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72684-5
Online ISBN: 978-3-540-72685-2
eBook Packages: Computer ScienceComputer Science (R0)