بهبود توازن بار در رایانش ابری با استفاده از الگوریتم جهش قورباغه سریع (R-SFLA )
محورهای موضوعی : فناوری اطلاعات و ارتباطاتکیومرث سلیمی 1 , مهدی ملامطلبی 2 *
1 - گروه کامپیوتر، واحد بوئین زهرا، دانشگاه آزاد اسلامی، بوئین زهرا، ایران
2 - گروه مهندسی کامپیوتر و فناوری اطلاعات، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
کلید واژه: رایانش ابری, توازن بار, الگوریتم جهش قورباغه, زمانبندی منابع, زمان پاسخ,
چکیده مقاله :
امروزه رایانش ابری به علت ارائه خدمات متنوع، کاربردهای زیادی دارد. از سوی دیگر، به علت رشد سریع، محدودیت منابع و هزینه نهایی، چالشهای متعددی در رایانش ابری به وجود آمده است که یکی از این چالشها، توازن بار است. منظور از توازن بار، چگونگی مدیریت توزیع بار در بین گرههای پردازشی، بهمنظور استفاده بهینه از منابع و صرف کمترین زمان جهت پاسخ به درخواست کاربر است. روشهای متعددی در خصوص برقراری توازن بار پیشنهاد شدهاند که یکی از آنها، الگوریتم جهش قورباغه است که پویا، تکاملی و الهام گرفته از طبیعت میباشد. در این مقاله، بهبودی بر الگوریتم جهش قورباغه پیشنهاد شده است که باعث همگرایی سریع و بستن راه حلقه تکرار تکامل معیوب قورباغهها، میگردد. جهت ارزیابی، الگوریتم جهش قورباغه بهبود یافته پیشنهادی R-SFLA و الگوریتم SFLA و الگوریتم ASFLA در شبیهساز کلودسیم تحت شرایط یکسان، مورد آزمایش قرار گرفتند. نتایج بهدستآمده از آزمایشات، بیانگر آن است که روش پیشنهادی نسبت به روشهای دیگر، از نظر هزینه کلی اجرا، زمان پاسخ و درجه توازن بار، کاراتر عمل نموده است.
Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of the load distribution among the processing nodes in order to have the best usage of resources while having minimum response time for the users’ requests. Several methods for load balancing in Cloud computing have been proposed in the literature. The shuffled frog leaping algorithm for load balancing is a dynamic, evolutionary, and inspired by nature. This paper proposed a modified rapid shuffled frog leaping algorithm (R-SFLA) that converge the defective evolution of frogs rapidly. In order to evaluate the performance of R-SFLA, it is compared to Shuffled Frog Leaping Algorithm (SFLA) and Augmented Shuffled Frog Leaping Algorithm (ASFLA) by the overall execution cost, Makespan, response time, and degree of imbalance. The simulation is performed in CloudSim, and the results obtained from the experiments indicated that the proposed algorithm acts more efficient compared to other methods based on the above mentioned factors.
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