بهبود تخصیص منابع در محاسبات لبه موبایل با استفاده از الگوریتم بهینه سازی ازدحام ذرات و گرگ خاکستری
محورهای موضوعی : فناوری اطلاعات و ارتباطات
سیدابراهیم دشتی رحمت آبادی
1
(هیات علمی)
سعید شب بویی
2
(دانشگاه آزاد اسلامی)
کلید واژه: بهبود تخصیص منابع , محاسبات لبه موبایل , الگوریتم بهینه سازی ازدحام ذرات, الگوریتم گرک خاکستری.,
چکیده مقاله :
محاسبات لبه موبایل تجربه کاربران نهایی را برای دستیابی به خدمات مناسب و کیفیت خدمات بهبود می دهد. در این مقاله مسئله بهبود تخصیص منابع هنگام بارگیری وظایف براساس دستگاه‌های تلفن همراه به سرورهای لبه در سیستم‌های محاسباتی بررسی می شود. برخی وظایف به صورت محلی پردازش و برخی به سرورهای لبه بارگذاری می شوند. مسئله اصلی این است که وظایف تخلیه‌شده برای ماشین‌های مجازی در شبکه های محاسباتی بصورت مناسب زمانبندی شوند تا زمان محاسبات، هزینه خدمات، اتلاف شبکه های محاسباتی و حداکثر ارتباط یک کار با شبکه به حداقل برسد. در این مقاله الگوریتم ترکیبی ازدحام ذرات و گرگ خاکستری برای مدیریت تخصیص منابع و زمانبندی وظایف برای دستیابی به یک نتیجه بهینه در شبکه های محاسبات لبه معرفی شد. نتایج مقایسه نشان دهنده بهبود زمان انتظار و هزینه در رویکرد پیشنهادی است. نتایج نشان می دهد که به طور میانگین مدل پیشنهادی با کاهش 10 درصدی زمان انجام کار و افزایش استفاده از منابع به میزان 16 درصد بهتر عمل کرده است.
Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigated. Some tasks are processed locally and some are offloaded to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, it was introduced using the hybrid algorithm of particle swarm and gray wolf to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%.
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49. عارفیان، زهرا و خیام باشی، محمدرضا،1400،مقایسه کاهش تاخیر در ارتباطات از نوع ماشین با استفاده از محاسبات مه و محاسبات ابری.
50. طهماسبی پویا، نیلوفر و صرام، مهدی آقا،1400،بهبود توازن بار در محاسبات مه با استفاده از الگوریتم Q-Learning،Fifth National Conference on ComputerEngineering،https://civilica.com/doc/1281556