تکنیک هوشمند مبتنی بر الگوریتم چتر دریایی برای زمانبندی وظایف بر اساس اولویت در شبکههای IoT/Fog
محورهای موضوعی : مهندسی برق و کامپیوترسکینه سهرابی 1 , مهدی سخائی نیا 2 * , محمد نصیری 3 , رضا محمدی 4
1 - گروه مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران
2 - گروه مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران
3 - گروه مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران
4 - گروه مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران
کلید واژه: اینترنت اشیاء, محاسبات مه, الگوریتم تخصیص منابع, الگوریتم چتر دریایی, الگوریتم ژنتیک,
چکیده مقاله :
محاسبات مه به منظور بهبود کیفیت خدمات برنامههای کاربردی اینترنت اشیا در محاسبات ابری معرفی شده است، انتقال حجم عظیم داده توسط دستگاههای اینترنت اشیا منجر به تأخیر بیشتر و سربار پهنای باند در محاسبات ابری شده است. از سوی دیگر دستگاههای اینترنت اشیا منجر به تولید حجم زیادی از دادهها برای پردازش شده است که به نوبه خود بار روی شبکه محاسباتی مه – ابر را افزایش میدهد و مشکلاتی را ایجاد میکند که نیازمند مدیریت منابع در این محیط است. پیچیدگی و تنوع وظایف و منابع درگیر در لایه مه، ارائه یک استراتژی مناسب برای زمانبندی وظایف را دشوار میکند. هدف زمانبندی وظایف، به حداکثر رساندن تخصیص و اجرای وظایف تحت سیستمهای محاسباتی مه - ابر و در عین حال در نظر گرفتن محدودیتهای مربوطه است. بنابراین، این مطالعه یک الگوریتم زمانبندی کار پیشرفته با استفاده از الگوریتم چتر دریایی و الگوریتم ژنتیک برای کاهش مصرف انرژی و زمان اجرا پیشنهاد کرده است تا اولویت وظایف رعایت شود. نتایج شبیهسازی و مقایسهی الگوریتم پیشنهادی با الگوریتمهای پیشرفته مورد بررسی در این مقاله برتری روش پیشنهادی در کاهش زمان اجرا و مصرف انرژی را تایید میکند.
Fog computing was implemented to enhance the service quality of IoT applications in cloud computing. The increase in latency and bandwidth overhead in cloud computing is a result of IoT devices transmitting large amounts of data. IoT devices are responsible for producing significant volumes of data that need to be managed, resulting in higher demands on the fog-cloud computing network and necessitating resource management in this setting. In the fog-cloud computing system, tasks of varying priorities can be dispatched for processing. Providing an appropriate scheduling strategy for tasks in the fog layer is challenging due to the multitude of tasks and resources involved. Task scheduling in fog-cloud computing systems aims to optimize task allocation and execution while considering relevant constraints. Thus, this research suggests an enhanced method using the jellyfish algorithm and genetic algorithm to minimize energy consumption and execution time while considering task priority, which is crucial in task allocation. The results of the simulation and comparison of the suggested algorithm with other algorithms in this study support the effectiveness of the proposed method in decreasing both execution time and energy usage.
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