ارائه یک مدل آگاه از انرژی و مبتنی بر زنجیره مارکوف به منظور مدیریت پویای ماشینهای مجازی در مراکز داده ابری
محورهای موضوعی : مهندسی برق و کامپیوترمهدی رجب زاده 1 , ابوالفضل طرقی حقیقت 2 * , امیرمسعود رحمانی 3
1 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات،گروه مهندسی كامپيوتر
2 - دانشگاه آزاد اسلامی واحد قزوین،دانشکده مهندسی کامپیوتر
3 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات،گروه مهندسی كامپيوتر
کلید واژه: الگوریتمهای فرااکتشافی, رایانش ابری, زنجیره مارکوف جاذب, کاهش مصرف انرژی,
چکیده مقاله :
استفاده از راهکارهای آگاه از انرژی از موضوعات مهم تحقیقاتی در حوزه رایانش ابری است. با کاربرد مؤثر الگوریتمهای جایگذاری و تجمیع ماشینهای مجازی، تأمین‌کنندگان ابر قادر خواهند بود مصرف انرژی را کاهش دهند. در این مقاله مدل جدیدی ارائه شده که با بهبود در الگوریتم‌ها و ارائه روش‌های مناسب، به دنبال رسیدن به نتایج مطلوب است. نظارت دوره‌ای بر وضعیت منابع، تحلیل مناسب داده‌های به دست آمده و پیش‌بینی وضعیت بحرانی سرورها به کمک مدل مارکوف پیشنهادی سبب شده است که تا حد امکان از تعداد مهاجرت‌های غیر ضروری کاسته شود. ترکیب الگوریتمهای ژنتیک و شبیه‌سازی تبرید در بخش جایگزینی در کنار تعریف زنجیره مارکوف جاذب باعث عملکرد بهتر و سریع‌تر الگوریتم پیشنهادی گردیده است. شبیه‌سازی‌های انجام‌شده در سناریوهای مختلف در کلودسیم نشان می‌دهد که در مقایسه با بهترین الگوریتم مورد مقایسه قرار گرفته، در بار کم، متوسط و زیاد، مصرف انرژی کاهش قابل توجهی داشته و این در حالی است که نقض توافقات سطح سرویس‌دهی نیز به طور متوسط 17 درصد کاهش یافته است.
The use of energy-conscious solutions is one of the important research topics in the field of cloud computing. By effectively using virtual machine placement and aggregation algorithms, cloud suppliers will be able to reduce energy consumption. In this paper, a new model is presented that seeks to achieve the desired results by improving the algorithms and providing appropriate methods. Periodic monitoring of resource status, proper analysis of the data obtained, and prediction of the critical state of the servers using the proposed Markov model have reduced the number of unnecessary migrations as much as possible. The combination of genetic algorithm and simulated annealing in the replacement section along with the definition of the adsorbent Markov chain has resulted in better and faster performance of the proposed algorithm. Simulations performed in different scenarios in CloudSim show that compared to the best algorithm compared, at low, medium and high load, energy consumption has decreased significantly. Violations of service level agreements also fell by an average of 17 percent.
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