بهبود طول عمر سیستمهای نهفتهی بیدرنگ به کمک زمانبندی آگاه از وضعیت باتری
محورهای موضوعی : مهندسی برق و کامپیوترصغری منوچهری 1 * , مهدی کارگهی 2
1 - دانشگاه تهران
2 - دانشگاه تهران
کلید واژه: سیستمهای نهفته بیدرنگ تغییر پویای ولتاژ زمانبندی آگاه از وضعیت باتری مصرف توان,
چکیده مقاله :
بسیاری از سیستمهای نهفته و دستگاههای متحرک برای تأمین انرژی مورد نیاز خود از باتری استفاده میکنند و بنابراین طول عمر این دستگاهها به طول عمر باتری وابسته است. بر این اساس، جهت افزایش میزان بهرهوری از این گونه سیستمها، کاهش مصرف انرژی و مدیریت نحوه استفاده از باتری اهمیت زیادی دارند. با توجه به خصوصیات و رفتار غیر خطی باتری، بیشینهکردن طول عمر باتری که به الگوی تخلیه آن نیز وابسته است از مسایل سخت محسوب میگردد. این مقاله جهت افزایش طول عمر سیستم و بیشینهکردن بهرهوری از باتری، به ارائه یک الگوریتم زمانبندی آگاه از وضعیت باتری برای وظایف دورهای در سیستمهای بیدرنگ مبتنی بر باتری میپردازد. در الگوریتم پیشنهادی یک روش ابتکاری حریصانه برای تغییر پویای ولتاژ با توجه به خصوصیات باتری و توان مصرفی وظایف ارائه میگردد. الگوریتم ارائهشده با دو روش ارزیابی میشود، در روش اول از تابع هزینه مبتنی بر شارژ مصرفی باتری استفاده میشود و در روش دوم از یک شبیهساز سطح پایین باتریهای لیتیوم- یون به نام Dualfoil بهرهبرداری خواهد شد. نتایج نشان میدهد که الگوریتم پیشنهادی منجر به افزایش طول عمر سیستم بین 6/19- 3/4 درصد در شرایط مختلف (از نظر بار کاری سیستم و محدوده توان مصرفی وظایف) شده است.
Many embedded systems and mobile devices use batteries as their energy suppliers. The lifetime of these devices is thus dependent on the battery behavior. Accordingly, battery management besides reducing the energy consumption of the respective system helps to increase the efficiency of such systems. Maximizing the battery lifetime is a quiet challenging problem due to the nonlinear behavior of batteries and its dependence on the characteristics of the discharge profile. This paper employs dynamic voltage scaling (DVS) to extend the lifetime of battery-operated real-time embedded systems. We propose a battery-aware scheduling algorithm to maximize the lifetime and efficiency of the battery. The proposed algorithm is based on greedy heuristics suggested by battery characteristic and power consumption of tasks to employs DVS. Two methods are used to evaluate the mentioned algorithms; the first one is based on the cost function derived from a high-level analytical model of battery, and the second one is based on Dualfoil, a low-level li-ion battery simulator. Experimental results show that the system lifetime can be increased about 4.3% to 19.6%in various situations (in terms of system workload and tasks power consumption).
[1] T. L. Martin, Balancing Batteries, Power, and Performance: System Issues in CPU Speed-Setting for Mobile Computing, Doctoral Dissertation, Carnegie Mellon University, 1999.
[2] L. Benini, et al., "A discrete-time battery model for high-level power estimation," in Proc. of Design, Automation and Test in Europe Conference and Exhibition, vol. ???, pp. 35-39, Paris, France, 27-30 Mar. 2000.
[3] D. Rakhmatov, Modeling and Optimization of Energy Supply and Demand for Portable Reconfigurable Electronic Systems, Ph.D. Thesis, Dept. Electr. Comput. Eng., Arizona Univ., Tucson, May 2002.
[4] L. Benini, G. Castelli, A. Macii, and R. Scarsi, "Battery-driven dynamic power management," IEEE Design and Test, vol. 18, no. 2, pp. 53-60, Mar. 2001.
[5] T. F. Fuller, M. Doyle, and J. Newman, "Simulation and optimization of the dual lithium ion insertion cell," J. of the Electrochemical Society, vol. 141, no. 1, pp. 1-10, 1994.
[6] M. Doyle, T. F. Fuller, and J. Newman, "Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell," J. of the Electro - Chemical Society, vol. 140, no. 6, pp. 1526-1533, Jun. 1993.
[7] T. F. Fuller, M. Doyle, and J. Newman, "Relaxation phenomena in lithium-ion insertion cells," J. of the Electrochemical Society, vol. 141, no. 4, pp. 982-990, Apr. 1994.
[8] "FORTRAN programs for the simulation of electrochemical systems," Jun. 2010, [Online]. Available: http://www.cchem.berkeley.edu/jsngrp/fortran.html.
[9] P. D. Vidts and R. E. White, "Mathematical modeling of a nickel-cadmium cell: proton diffusion in the nickel electrode," J. of the Electrochemical Society, vol. 142, no. 1, pp. 1509-1519, May 1995.
[10] E. Podlaha and H. Cheh, "Modeling of cylindrical alkaline cells," J. of the Electrochemical Society, vol. 141, no. 1, pp. 15-27, Jan. 1994.
[11] "The spice page," Jun. 2010, [Online]. Available: http://bwrc.eecs.berkeley.edu/classes/icbook/spice/.
[12] S. C. Hageman, "Simple PSpice models let you simulate common battery types," Electronic Design News, vol. 38, no. 1, pp. 117-129, 1993.
[13] S. Gold, "A PSpice macromodel for lithium-ion batteries," in Proc. of the 12th Annual Battery Conf. on Applications and Advances, pp. 215-222, 14-17 Jan 1997.
[14] M. R. Jongerden and B. R. Haverkort, Battery Modeling, Centre for Telematics and Information Technology, University of Twente, Enschede, 2008.
[15] D. Rakhmatov and S. Vrudhula, "Energy management for battery-powerd embedded systems," ACM Trans. on Embedded Computing Systems, vol. 2, no. 3, pp. 277-432, Aug. 2003.
[16] C. Chiasserini and R. Rao, "Energy efficient battery management," IEEE J. on Selected Areas in Communications, vol. 19, no. 7, pp. 1235-1245, Jul. 2001.
[17] D. Rakhmatov, S. Vrudhula, and D. A. Wallach, "Battery lifetime predictions for energy-aware computing," in Proc. of the Int. Symp.on Low Power Electronics and Design, ISLPED '02, pp. 154-159, 2002.
[18] J. Manwell and J. McGowan, "Lead acid battery storage model for hybrid energy systems," Solar Energy, vol. 50, no. 5, pp. 399-405, May 1993.
[19] C. Chiasserini and R. Rao, "A model for battery pulsed discharge with recovery effect," in Proc. of Wireless Communications and Networking Conf., vol. 2, pp. 636-639, 21-24 Sep. 1999.
[20] C. Chiasserini and R. Rao, "Pulsed battery discharge in communication devices," in Proc. of the 5th Int. Conf. on Mobile Computing and Networking, pp. 88-95, 1999.
[21] C. Chiasserini and R. Rao, "Improving battery performance by using traffic shaping techniques," IEEE J. on Selected Areas in Communications, vol. 19, no. 7, pp. 1385-1394, Jul. 2001.
[22] V. Rao, G. Singhal, A. Kumar, and N. Navet, "Battery model for embedded systems," in Proc. of Int. Conf. on VLSI Design, 105-110, Jan. 2005.
[23] V. Rao, G. Singhal, A. Kumar, and N. Navet, "Stochastic battery model for embedded systems," in Proc. 18th In. Conf. on VLSI Design, 2005.
[24] M. R. Jongerden and B. R. Haverkort, "Which battery model to use?" in Proc. of the 24th UK Performance Engineering Workshop, (UKPEW), Technical Report Series of the Department of Computing, Imperial College London, pages 76-88, 2008.
[25] T. Yokoyama, G. Zeng, H. Tomiyama, and H. Takada, "Static task scheduling algorithms based on greedy heuristics for battery-powerd DVS Systems," IEICE Trans. on Information and Systems, vol. E93-D, no. 10, pp. 2737-2746, Oct. 2010.
[26] P. Chowdhury and C. Chakrabarti, "Static task-scheduling algorithms for battery-powered DVS systems," IEEE Trans. on Very Large Scale Integration Systems, vol. 13, no. 2, pp. 226-237, Feb. 2005.
[27] J. Luo and N. K. Jha, "Battery-aware static scheduling for distributed real-time embedded systems," in Proc. of the 38th Conf. on Design Automation, pp. 134-139, Jun. 2001.
[28] A. Lahiri, S. Agarwal, A. Basu, and B. B. Bhattacharya, "Recovery-based real-time static scheduling for battery life optimization," in Proc. of the 19th Int. Conf. on VLSI Design, pp. 59-64, 2006.
[29] J. Zhuo and C. Chakrabarti, "An efficient dynamic task scheduling algorithm for battery powered DVS systems," in Proc. Asia and South Pacific Design Automation Conf., pp. 846-849, Shanghai, China, 18–21 January 2005.
[30] V. Rao, G. Singhal, N. Navet, A. Kumar, and G. S. Visweswaran, "Battery aware dynamic scheduling for periodic task graphs," in 20th Int. Parallel and Distributed Processing Sym., 25-29 April 2006.
[31] D. Rakhmatov and S. Vrudhula, "An analytical high-level battery model for use in energy management of portable electronic systems," in Proc. of the Int. Conf. on Computer Aided Design, pp. 488-493, 2001.
[32] A. Anderi, et al., "Overhead-conscious voltage selection for dynamicand leakage energy reduction of time-constrained systems," IEEE Proc. of Computer and Digital Techniques, vol. 152, no. 1, pp. 28-38, 14 Jan. 2005.