تامین پویای سرویس در محیط مه مبتنی بر اتوماتای یادگیر و الگوریتم ژنتیک چندهدفه

جامعی, سید مهدی

doi:10.22052/scj.2023.248643.1115

تامین پویای سرویس در محیط مه مبتنی بر اتوماتای یادگیر و الگوریتم ژنتیک چندهدفه

نوع مقاله : مقاله پژوهشی

نویسنده

سید مهدی جامعی

گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران

10.22052/scj.2023.248643.1115

چکیده

امروزه تعداد برنامه‌های کاربردی که نیاز به زمان پاسخ‌دهی کمی دارند، روز به روز در حال افزایش است و بکارگیری محیط مه اخیرا مورد توجه زیادی قرار گرفته است. با توجه به پویایی استفاده از منابع در اکثر برنامه‌های اینترنت اشیا، نمی‌توان مکان ثابتی برای قرارگیری و اجرای سرویس‌ها در محیط مه در نظر گرفت و بنابراین باید سرویس‌ها در محیط مه به صورت پویا قرار داده شوند. مساله قرار دادن سرویس‌های مورد نیاز اینترنت اشیا در دستگاه‌های مه با محدودیت منابع، به عنوان یک مساله NP-hard شناخته می‌شود. در این مقاله، روشی پویا مبتنی بر الگوریتم ژنتیک چندهدفه با رتبه‌بندی نامغلوب جهت حل این مساله ارائه می‌گردد. در روش پیشنهادی، از اتوماتای یادگیر، جهت بهبود رفتار ژنتیکی و تنظیم پویای نرخ جهش و تقاطع استفاده می‌شود. روش پیشنهادی با استفاده از نرم‌افزار iFogsim شبیه‌سازی شده و نتایج شبیه‌سازی نشان می‌دهد روش پیشنهادی با در نظر گرفتن همزمان سه معیار تاخیر سرویس، هزینه و انرژی مصرفی، کارایی بهتری را نسبت به الگوریتم‌های مورد مقایسه دارد. از نظر هزینه، در مقایسه با دو روش CSA و LRFC به‌ترتیب 11 و 21 درصد کاهش داشته است. همچنین روش پیشنهادی از نظر میانگین تاخیر سرویس‌دهی نسبت به دو روش CSA و HAFA به‌ترتیب 7 و 15 درصد کاهش داشته است. از نظر انرژی مصرفی نیز روش پیشنهادی نسبت به روش‌های دیگر بهبود حداقل 8 درصدی را نشان می‌دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Dynamic Service Provisioning in Fog Environment based on Learning Automata and Multi-objective Genetic Algorithm

نویسنده [English]

Seyed Mahdi Jameii

Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.

چکیده [English]

Nowadays, the number of applications that require a short response time is increasing, and utilizing fog environment has recently received a lot of attention. Due to the dynamics of the resource usage pattern in most Internet of Things applications, a fixed location cannot be considered for the placement and execution of services in the fog environment, and therefore the services must be dynamically placed in the fog environment. The problem of placing required Internet of Things services in cloud devices with limited resources is known as an NP-hard problem. In this article, a dynamic method based on multi-objective genetic algorithm with non-dominated ranking is presented to solve this problem. In the proposed method, learning automata are used to improve genetic behavior and dynamically adjust mutation and crossover rates. The proposed method is simulated using iFogsim and the simulation results show that the proposed method has a better efficiency than the compared algorithms by simultaneously considering the three criteria of service delay, cost and energy consumption. In terms of cost, compared to the two CSA and LRFC methods, it has decreased by 11% and 21%, respectively. Also, in terms of the average service delay, compared to the CSA and HAFA methods, the proposed method has decreased by 7% and 15 %, respectively. In terms of energy consumption, the proposed method shows an improvement of at least 8% compared to other methods.

کلیدواژه‌ها [English]

IoT
Fog Computing
Cloud Computing
Dynamic Service Provisioning
Genetic Algorithm
Learning Automata

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