زمان‌بندی کاربردهای جریان کاری علمی در محیط چندابری با استفاده از الگوریتم جستجوی فاخته
محورهای موضوعی : مهندسی برق و کامپیوترسمیه محمدی 1 , لطیف پورکریمی 2 , سمیه عبدی 3
1 - دانشگاه آزاد اسلامی واحد کرمانشاه
2 - دانشگاه رازی
3 - دانشگاه آزاد اسلامی واحد اسلام آباد غرب
کلید واژه: چندابریزمان‌بندیجریان کاری علمی بهینه‌سازی هزینهالگوریتم جستجوی فاخته,
چکیده مقاله :
محیط‌های چندابری شامل منابع متنوع قابل ملاحظه‌ای هستند که هزینه‌های زمان‌بندی کاربردهای جریان کاری در چنین محیط‌هایی می‌تواند به طور چشم‌گیری کاهش یابد و همچنین محدودیت ارائه منابع توسط فراهم‌کنندگان تجاری ابر رفع شود. بر این اساس، این تحقیق به مسأله زمان‌بندی کاربردهای جریان کاری علمی در محیط چندابری تحت قید مهلت زمانی با هدف کمینه‌سازی هزینه می‌پردازد. در اين مقاله با به كارگيري الگوريتم جستجوي فاخته که يكي از مشهورترین روش‌هاي جستجوي فراابتكاري می‌باشد، الگوريتمي براي مسأله زمان‌بندی کاربردهای جریان کاری در محیط چندابری ارائه شده است. الگوريتم فراابتكاري جستجوي فاخته قادر است در مدت زماني كوتاه فضاي جواب را جستجو نموده و جواب‌هايي را در همسايگي جواب بهینه سراسری بيابد كه به آن نزديك مي‌باشد. نتایج به دست آمده نشان می‌دهند که راهکار پیشنهادی این تحقیق در مقایسه با دیگر راهکارهای فراابتکاری در موارد کاهش هزینه کارایی بهتری داشته و همچنین جواب‌هاي به دست آمده از الگوريتم فراابتکاری پیشنهادي، در حد مطلوبی نزديک به جواب‌هاي بهینه سراسری به دست آمده از مدل رياضی است.
Multi-cloud environments consist of the considerable variety of resources where the cost of scheduling workflow applications can be significantly reduced in such environments and the resource limitationsimposed by commercial cloud providers can bealso overcome. Accordingly, this study addresses the scheduling of scientific workflowapplications in a multi-cloud environment under a deadline with the aim of minimizing costs. In this paper,an algorithm for scheduling of workflow applications in multi-cloud environment is presented using the cuckoo search algorithm which is one of the most popular meta-heuristic methods. The Cuckoo Search Algorithm is able to search the solution space in a short time and find solutions in the vicinity of the optimal global solution that is close to it. The results show that the proposed approach of this research has better performance in comparison with other meta- heuristic approach in terms of cost reduction. Moreover, the obtained solutions of the proposed meta- heuristic algorithm are in a desirable degree close to the global optimal solutions of mathematical model.
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