Abstract
Energy management is an important issue in today's real-time systems due to the high costs of energy supplying. Using renewable, like wave, wind, and solar energy sources seem promising methods to address this issue. However, because of the existing contrast between the critical nature of hard real-time systems and the unpredictable nature of renewable energies, some supplementary energy source like electricity grid or battery is needed. In this paper, we consider hard real-time systems with two renewable and nonrenewable energy sources. In order to reduce the costs, we present two dynamic voltage scaling controllers to minimize the energy attained from the latter source. In order to handle variations of the environmental energy and workload, the model predictive control approach is employed. One nonlinear approach beside one fast linear piecewise affine explicit controller are proposed. The efficacies of the proposed approaches have been investigated through extensive simulations. Comparisons to an ideal clairvoyant controller as a baseline show that, in the studied scenarios, the proposed controllers guarantee at least 78% of the baseline performance.
- Abdeddaim, Y. and Masson, D. 2012. Real-time scheduling of energy harvesting embedded systems with timed automata. In Proceedings of the Conference on Embedded and Real-Time Computing Systems and Applications. 31--40. Google Scholar
Digital Library
- Allavena, A. and Mosse, D. 2001. Scheduling of frame-based embedded systems with rechargeable batteries. In Proceedings of the Workshop on Power Management for Real-Time and Embedded Systems (in conjunction with RTAS'01).Google Scholar
- Aydin, H., Melhem, R., Mossé, D., and Mejía-Alvarez, P. 2004. Power-aware scheduling for periodic real-time tasks. IEEE Trans. Comput. 53, 5, 584--600. Google Scholar
Digital Library
- Baotic, M., Christophersen, F. J., and Morari, M. 2003. A new algorithm for constrained finite time optimal control of hybrid systems with a linear performance index. In Proceedings of the European Control Conference. 3335--3340.Google Scholar
- Bartolini, A., Cacciari, M., Tilli, A., and Benini, L. 2011. A distributed and self-calibrating model-predictive controller for energy and thermal management of high-performance multicores. In Proceedings of the European Conference on Circuit Theory and Design. 1--6.Google Scholar
- Bemporad, A., Borrelli, F., and Morari, M. 2000. Explicit solution of LP-based model predictive control. In Proceedings of the Conference on Decision and Control. 632--637.Google Scholar
- Bemporad, A., Morari, M., Dua, V., and Pistikopoulos, E. N. 2002. The explicit linear quadratic regulator for constrained systems. Automatica 38, 1, 3--20. Google Scholar
Digital Library
- Borrelli, F., Baotic, M., Bemporad, A., and Morari, M. 2003. An efficient algorithm for computing the state feedback optimal control law for discrete time hybrid systems. In Proceedings of the American Control Conference. 4717--4722.Google Scholar
- Camacho, E. F. and Bordons, C. 2004. Model Predictive Control. Vol. 2, Springer.Google Scholar
- Celik, A. N. 2004. A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy 29, 4, 593--604.Google Scholar
Cross Ref
- Chandarli, Y., Abdeddaïm, Y., and Masson, D. 2012. The fixed priority scheduling problem for energy harvesting real-time systems. In Proceedings of the Conference on Embedded and Real-Time Computing Systems and Applications. 415--418. Google Scholar
Digital Library
- Chen, Y., Lu, C., and Koutsoukos, X. 2007. Optimal discrete rate adaptation for distributed real-time systems. In Proceedings of the Real-Time Systems Symposium. 181--192. Google Scholar
Digital Library
- Clark, R., Jensen, E. D, Kanevsky, A., Maurer, J., Wallace, P., Wheeler, T., Zhang, Y., Wells, D., Lawrence, T., and Hurley, P. 1999. An adaptive, distributed airborne tracking sysem. Parallel Distri. Proces., 353--362. Google Scholar
Digital Library
- Clarke, D. W. 1994a. Advances in Model-Based Predictive Control. Vol. 4, Chapter Neural Network Based Predictive Control.Google Scholar
- Clarke, D. W. 1994b. Advances in Model-Based Predictive Control. Vol. 4, Chapter Fuzzy Predictive Control with Adaptive Gain.Google Scholar
- Clarke, D. W., Mohtadi, C., and Tuffs, P. S. 1987. Generalized predictive control--Part I. The basic algorithm. Automatica 23, 2, 137--148. Google Scholar
Digital Library
- Crop, I. 2004. Intel PXA270 processor electrical mechanical, and thermal specification. http://www.intel.com/design/embeddedpca/products/pxa270/techdocs.htm.Google Scholar
- Cutler, C. and Ramaker, B. L. 1980. Dynamic matrix control—a computer control algorithm. In Proceedings of the Joint Automatic Control Conference. 13--15.Google Scholar
- Diehl, M., Ferreau, H. J., and Haverbeke, N. 2009. Efficient numerical methods for nonlinear MPC and moving horizon estimation. Nonlinear Model Predictive Control 3, 1, 391--417.Google Scholar
Cross Ref
- Dubois-Ferrière, H., Fabre, L., Meier, R., and Metrailler, P. 2006. TinyNode: a comprehensive platform for wireless sensor network applications. In Proceedings of the Conference on Information Processing in Sensor Networks. 358--365. Google Scholar
Digital Library
- Eu, Z. A., Tan, H.-P., and Seah, W. K. G. 2010. Opportunistic routing in wireless sensor networks powered by ambient energy harvesting. Comput. Netw. 4, 17, 2943--2966. Google Scholar
Digital Library
- Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., and Draxl, C. 2011. The state-of-the-art in short-term prediction of wind power: A literature overview. Tech. rep. ANEMOS. plus.Google Scholar
- Grancharova, A. and Johansen, T. A. 2009. Explicit approximate model predictive control of constrained nonlinear systems with quantized input. Nonlinear Model Predictive Control 3, 1, 371--380.Google Scholar
Cross Ref
- Hsu, J., Zahedi, S., Friedman, J., Kansal, A., Raghunathan, V., and Srivastava, M. 2005. Heliomote: Enabling long-lived sensor networks through solar energy harvesting. In Proceedings of the Conference on Embedded Networked Sensor Systems. 309--317. Google Scholar
Digital Library
- Hsu, J., Zahedi, S., Kansal, A., Srivastava, M., and Raghunathan, V. 2006. Adaptive duty cycling for energy harvesting systems. In Proceedings of the International Symposium on Low Power Electronics and Design. 180--185. Google Scholar
Digital Library
- Iqdour, R. and Zeroual, A. 2007. Prediction of daily global solar radiation using fuzzy systems. Int. J. Sustainable Energy 26, 1, 19--29.Google Scholar
Cross Ref
- Ishihara, T. and Yasuura, H. 1998. Voltage scheduling problem for dynamically variable voltage processors. In Proceedings of the International Symposium on Low Power Electronics and Design. 197--202. Google Scholar
Digital Library
- Kansal, A., Hsu, J., Zahedi, S., and Srivastava, M. B. 2007. Power management in energy harvesting sensor networks. ACM Trans. Embedd. Comput. Syst. 6, 4, 32--66. Google Scholar
Digital Library
- Kansal, A., Potter, D., and Srivastava, M. B. 2004. Performance aware tasking for environmentally powered sensor networks. In Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems. ACM, 223--234. Google Scholar
Digital Library
- Kargahi, M. and Movaghar, A. 2011. Performance optimization based on analytical modeling in a real-time system with constrained time/utility functions. IEEE Trans. Comput. 60, 8, 1169--1181. Google Scholar
Digital Library
- Koch, P. 2009. How to interface energy harvesting models with multiprocessor scheduling paradigms. In Proceedings of the Conference on Wireless Communication. 21--25.Google Scholar
Cross Ref
- Kooti, H., Dang, N., Mishra, D., and Bozorgzadeh, E. 2012. Energy budget management for energy harvesting embedded systems. In Proceedings of the Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). 320--329. Google Scholar
Digital Library
- Kvasnica, M., Grieder, P., Baotić, M, and Morari., M. 2004. Multi-parametric toolbox (MPT). Hybrid Syst. Comput. Control 6, 2, 121--124.Google Scholar
- Liu, C. L. and Layland, J. W. 1973. Scheduling algorithms for multiprogramming in a hard-real-time environment. J. ACM 20, 1, 46--61. Google Scholar
Digital Library
- Liu, S., Lu, J., Wu, Q., and Qiu, Q. 2010. Load-matching adaptive task scheduling for energy efficiency in energy harvesting real-time embedded systems. In Proceedings of the International Symposium on Low Power Electronics and Design. ACM, 325--330. Google Scholar
Digital Library
- Liu, S., Qiu, Q., and Wu, Q. 2008. Energy aware dynamic voltage and frequency selection for real-time systems with energy harvesting. In Proceedings of the Conference on Design, Automation and Test in Europe. 236--241. Google Scholar
Digital Library
- Liu, S., Wu, Q., and Qiu, Q. 2009. An adaptive scheduling and voltage/frequency selection algorithm for real-time energy harvesting systems. In Proceedings of the Design Automation Conference. ACM, 782--787. Google Scholar
Digital Library
- Lu, J., Liu, S., Wu, Q., and Qiu, Q. 2010. Accurate modeling and prediction of energy availability in energy harvesting real-time embedded systems. In Proceedings of the International Green Computing Conference. 469--476. Google Scholar
Digital Library
- Moser, C., Brunelli, D., Thiele, L., and Benini, L. 2007a. Real-time scheduling for energy harvesting sensor nodes. Real-Time Syst. 37, 3, 233--260. Google Scholar
Digital Library
- Moser, C., Thiele, L., Brunelli, D., and Benini, L. 2007b. Adaptive power management in energy harvesting systems. In Proceedings of the Conference on Design, Automation and Test in Europe. 773--778. Google Scholar
Digital Library
- Moser, C., Thiele, L., Brunelli, D., and Benini, L. 2008. Robust and low complexity rate control for solar powered sensors. In Proceedings of the Conference on Design, Automation and Test in Europe. 230--235. Google Scholar
Digital Library
- Newman, J. S. 2004. Fortran programs for simulation of electrochemical systems, dualfoil program for lithium battery simulation. www.cchem.berkeley.edu/jsngrp/fortran.html.Google Scholar
- Piorno, J. R., Bergonzini, C., Atienza, D., and Rosing, T. S. 2009. Prediction and management in energy harvested wireless sensor nodes. In Proceedings of the Conference on Wireless Communication. 6--10.Google Scholar
- Qiu, M., Jia, Z., Xue, C., Shao, Z., and Sha, E. H.-M. 2007. Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP. J. VLSI Signal Process. Syst. 46, 1, 55--73. Google Scholar
Digital Library
- Qiu, M., Yang, L. T., Shao, Z., and Sha, E. H.-M. 2010. Dynamic and leakage energy minimization with soft real-time loop scheduling and voltage assignment. IEEE Trans. VLSI. Syst. 18, 3, 501--504. Google Scholar
Digital Library
- Ravinagarajan, A., Dondi, D., and Rosing, T. S. 2010. DVFS based task scheduling in a harvesting WSN for Structural Health Monitoring. In Proceedings of the Conference on Design, Automation and Test in Europe. 1518--1523. Google Scholar
Digital Library
- Richalet, J. 1993. Pratique de la commande prédictive. Hermes.Google Scholar
- Richalet, J., Rault, A., Testud, J. L., and Papon, J. 1978. Model predictive heuristic control: Applications to industrial processes. Automatica 14, 5, 413--428. Google Scholar
Digital Library
- Roundy, S., Steingart, D., Frechette, L., Wright, P., and Rabaey, J. 2004. Power sources for wireless sensor networks. Wirel. Sensor Netw. 42, 5, 1--17.Google Scholar
- Rusu, C., Melhem, R., and Mossé, D. 2005. Multi-version scheduling in rechargeable energy-aware real-time systems. J. Embed. Comput. 1, 2, 271--283. Google Scholar
Digital Library
- Seborg, D. E., Edgar, T. F., and Mellichamp, D. A. 2007. Process Dynamics & Control. Vol. 2, Wiley-India, Chapter Feedforward and Ratio Control.Google Scholar
- Steck, J. B. and Rosing, T. S. 2009. Adapting task utility in externally triggered energy harvesting wireless sensing systems. In Proceedings of the Conference on Networked Sensing Systems. 16--23. Google Scholar
Digital Library
- Stewart, C. and Shen, K. 2009. Some joules are more precious than others: Managing renewable energy in the datacenter. In Proceedings of the Workshop on Power Aware Computing and Systems (HotPower).Google Scholar
- Susu, A. E., Acquaviva, A., Atienza, D., and De Micheli, G. 2008. Stochastic modeling and analysis for environmentally powered wireless sensor nodes. In Proceedings of the Symposium on Modeling and Optimization in Mobile, ad Hoc, and Wireless Networks. 125--134.Google Scholar
- Tin, T., Sovacool, B. K., Blake, D., Magill, P., Naggar, S. E. N., Lidstrom, S., Ishizawa, K., and Berte, J. 2010. Energy efficiency and renewable energy under extreme conditions: Case studies from Antarctica. Renewable Energy 35, 8, 1715--1723.Google Scholar
Cross Ref
- Tschanz, J. W., Kao, J. T., Narendra, S. G., Nair, R., D. A., Antoniadis., Chandrakasan, A. P., and De, V. 2002. Adaptive body bias for reducing impacts of die-to-die and within-die parameter variations on microprocessor frequency and leakage. IEEE J. Solid-State Circuits 37, 11, 1396--1402.Google Scholar
Cross Ref
- Voigt, T., Dunkels, A., Alonso, J., Ritter, H., and Schiller, J. 2004. Solar-aware clustering in wireless sensor networks. In Proceedings of the Symposium on Computers and Communications. 238--243. Google Scholar
Digital Library
- Wang, X., Jia, D., Lu, C., and Koutsoukos, X. 2007. DEUCON: Decentralized end-to-end utilization control for distributed real-time systems. IEEE Trans. Parallel Distrib. Syst. 18, 7, 332--340. Google Scholar
Digital Library
- Wu. H. 2005. Energy-efficient, utility accrual real-time scheduling. Ph.D. Dissertation. Virginia Polytechnic Institute and State University. Google Scholar
Digital Library
- Xie, F., Martonosi, M., and Malik, S. 2004. Intraprogram dynamic voltage scaling: Bounding opportunities with analytic modeling. ACM Trans. Archit. Code Optim. 1, 3, 323--367. Google Scholar
Digital Library
- Zanini, F., Atienza, D., Benini, L., and De Micheli, G. 2009. Multicore thermal management with model predictive control. In Proceedings of the European Conference on Circuit Theory and Design. 711--714.Google Scholar
Cross Ref
- Zavala, V. M. and Biegler, L. T. 2009. Nonlinear programming strategies for state estimation and model predictive control. Nonlinear Model Predictive Control 3, 1, 419--432.Google Scholar
Cross Ref
Index Terms
Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources
Recommendations
Energy Guarantee Scheme for Real-time Systems with Energy Harvesting Constraints
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 ...
Energy-Adaptive Signal Processing Under Renewable Energy
This paper presents an energy-adaptive performance management technique for the design of embedded signal processing systems powered by renewable energy sources. By jointly considering the non-deterministic characteristics of renewable energy and the ...
Energy-Aware Fixed-Priority Multi-core Scheduling for Real-Time Systems
RTCSA '11: Proceedings of the 2011 IEEE17th International Conference on Embedded and Real-Time Computing Systems and Applications - Volume 01Multi-core processors are becoming the dominant choice due to energy and thermal considerations, which also applies to embedded and real-time systems. While fixed-priority scheduling with task-splitting in real-time systems are widely applied, current ...






Comments