بهبود طول عمر سیستم‌های نهفته‌‌ی بی‌درنگ به کمک زمان‌بندی آگاه از وضعیت باتری
محورهای موضوعی : مهندسی برق و کامپیوتر
صغری منوچهری
1
(دانشگاه تهران)
مهدی کارگهی
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).
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