بهینهسازی زمانبندی در سیستمهای ابری ناهمگن با الگوریتمهای کاپوچین و کلونی مورچگان
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
1 - گروه مهندسی کامپیوتر، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران
کلید واژه: محاسبات ابری , زمانبندی وظایف, الگوریتم جستجوی کاپوچین, بهینهسازی کلونی مورچگان معکوس,
چکیده مقاله :
رایانش ابری یک الگوی محاسباتی است که نیازهای محاسباتی و ذخیرهسازی کاربران نهایی را برآورده میکند. مراکز داده مبتنی بر ابر به دلیل افزایش تصاعدی تقاضای خدمات، به بهبود مستمر عملکرد خود نیاز دارند. زمانبندی وظایف بخشی ضروری از رایانش ابری برای دستیابی به استفاده بهینه از منابع استفاده بهینه از منابع، کاهش مصرف انرژی، حداقل زمان پاسخگویی و حداکثر توان است. الگوریتمهای زمانبندی در سیستمهای توزیع شده موازی برای زمانبندی وظایف و نگاشت آنها به منابع نقش مهمی را ایفا میکنند. در این مقاله یک رویکرد جدید برای مهاجرت ماشینهای مجازی با استفاده از الگوریتم بهینهسازی کاپوچین ارائه شده است. رویکرد پیشنهادی سعی دارد از نقاط قوت مهاجرت و زمانبندی با الگوریتم جستجوی کاپوچین استفاده کرده و با اتخاذ یک چارچوب تصمیمگیری بر اساس شرایط وظایف دریافتی، الگوریتم مناسبی را جهت اعمال به وظیفه بعدی انتخاب کند. رویکرد پیشنهادی توانسته است از نظر انرژی، تعادل بار، زمان اجرا و همچنین تعادل بار نسبت به روشهای قبل 15-20 درصد بهبود داشته باشد.
Cloud Computing (CC) is a computing paradigm to satisfy end users' computing and storage needs. Cloud data centers must continuously improve their performance due to the exponential rise in service demand. Task scheduling is an essential component of CC to achieve optimal resource utilization, reduced Energy Consumption (EC), minimum response time, and maximum efficiency. Scheduling algorithms are crucial for task scheduling and resource mapping in distributed and parallel systems. This study proposes a novel approach to migrating Virtual Machines (VMs) using a Capuchin Search Algorithm (CapSA). The proposed approach seeks to utilize the strengths of migration and scheduling based on a hybrid multi-objective CapSA and Inverted Ant Colony Optimization (IACO) algorithm and select an optimal algorithm to apply to the succeeding task by adopting a decision-making framework according to the received tasks' conditions. The proposed approach outperforms the earlier approaches regarding EC, Execution Time (ET), and load balancing by 15-20%
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