محاسبات ابری سبز با کاهش مصرف انرژی در مهاجرت زنده اولویت‌دار سرویس‌ها
محورهای موضوعی : مهندسی برق و کامپیوتر
محمد رستمی
1
(دانشگاه کاشان)
سلمان گلی
2
(دانشگاه کاشان)
کلید واژه: رایانش ابری سرویس‌های همکارکاهش مصرف انرژی مهاجرت زنده سرویس‌ها,
چکیده مقاله :
امروزه رشد سریع تقاضا برای استفاده از منابع محاسباتی ابری، موجب افزایش مصرف انرژی در مراکز داده شده است. محاسبات ابری سبز برای جلوگیری از افزایش مصرف انرژی مراکز داده بیان شده است. یکی از راهکارهای کاهش مصرف انرژی در سیستم‌های پردازش ابری، استفاده از روش‌های تجمیع سرویس‌ها است. روش‌های تجمیع موجود با مهاجرت‌های غیر ضروری، عدم تعادل بار کاری میزبان‌ها و نادیده‌گرفتن ارتباط بین سرویس‌ها ممکن است باعث کاهش کیفیت سرویس و افزایش مصرف انرژی شود. لذا در این تحقیق با مهاجرت‌دادن سرویس‌های ضروری بر اساس اولویت (شامل تعداد فرزندان، سطح و هزینه ارتباطی هر سرویس)، از میزبان‌هایی که بارکاری خیلی زیاد و یا خیلی کم دارند (که موجب مصرف انرژی زیادی می‌شوند) به میزبان‌هایی که حاوی سرویس‌های همکار هستند، بهره‌وری منابع موجود مرکز داده بهبود یافته و مصرف انرژی کاهش می‌یابد. مهاجرت زنده سرویس‌ها بر اساس اولویت و به حداقل رساندن تعداد مهاجرت‌ها با هدف کاهش مصرف انرژی، کاهش زمان پاسخ و افزایش کارایی سيستم می‌باشد. با بررسی و مقایسه روش پیشنهادی با روش‌های دیگر، بهبود 37/10 درصدی در کاهش مصرف انرژی در مراکز داده ابری، 18/11 درصدی در کاهش تعداد مهاجرت سرویس‌ها و 46/1 درصدی در افزایش تعداد میزبان‌هایی که خاموش شده‌اند، مشاهده می‌شود.
Today, the rapid growth in cloud computing resources usage has increased energy consumption in data centers. Green cloud computing goal is to decrease the energy consumption of data centers. In the meantime, service aggregation is a good method to reduce energy consumption in these systems. Existing aggregation methods with unnecessary migration, the unbalanced workload of hosts, and ignoring the relationship between services may reduce the quality of service and increase energy consumption. Therefore, in this study, by migrating the necessary services based on priority (including the number of children, the level and communication cost of each service), from hosts with the unbalanced workload to hosts that contain partner services, the productivity of available resources is improved and the energy consumption is decreased. Live services migration based on prioritizing and minimizing the number of migrations can also lead to response time decrease and system efficiency increase. The proposed method can lead to an 11.79% decrease in energy consumption, a 12.15% reduction in the number of service migrations, and a 1.55% increase in the number of hosts that have been shut down.
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