رویکردی کارآمد برای تامین پویای سرویس¬های اینترنت اشیاءروی زیرساخت رایانش مه
محورهای موضوعی : مهندسی کامپیوتر و فناوری اطلاعاتميثم تكيه بند 1 , مصطفي قبائي آراني 2 * , علی شهیدی نژاد 3
1 - گروه مهندسي كامپيوتر، واحد قم، دانشگاه آزاد اسلامي، ايران
2 - گروه مهندسي كامپيوتر، واحد قم، دانشگاه آزاد اسلامي، ايران
3 - گروه مهندسی کامپیوتر و فناوری اطلاعات، داشگاه آزاد قم
کلید واژه: رایانش مه, تامین پویای سرویس, اتوماتای یادگیر, برنامه¬های کاربردی اینرتنت اشیاء, تاخیر سرویس.,
چکیده مقاله :
پیشرفت‌های اخیر در فناوری اینترنت اشیاء منجر به گسترش کاربردهای آن در حوزه‌های مختلفی مانند ساختمان‌ها، شهرها، مراقبت‌های بهداشتی و غیره شده است. رایانش مه به عنوان یک پلتفرم توزیع‌شده در لبه شبکه، امکان اجرای برنامه‌های کاربردی اینترنت اشیاء را به صورت بلادرنگ فراهم می‌کند. به دلیل پویایی محیط مه و تغییرات مداوم در رفتار برنامه‌های اینترنت اشیاء، یکی از چالش‌های اصلی، تامین بهینه و پویای سرویس‌های اینترنت اشیاء بر روی منابع موجود در محیط مه است. این مطالعه با هدف ارائه یک مکانیزم تامین سرویس بهینه و پویا، از تکنیک‌های یادگیری تقویتی مانند اتوماتای یادگیر استفاده کرده است. چارچوب پیشنهادی این مطالعه شامل یک معماری سه‌لایه (اشیاء، مه و ابر) و یک مولفه مدیر تامین سرویس پویا است که از سه زیرمولفه مانیتورینگ درخواست‌های سرویس و وضعیت گره‌های مه، تحلیل‌گر بار کاری و ارائه‌دهنده سرویس، تشکیل شده و وظیفه تامین سرویس‌های مورد نیاز در لایهی مه را بر عهده دارد. این مکانیزم در سه سناریوی مختلف ارزیابی شده است که شامل بررسی جریان ترافیک واقعی، مقایسه با راه‌حل‌های بهینه و تاثیر آستانه تاخیر سرویس می‌شود. نتایج شبیه‌سازی نشان می‌دهد که روش پیشنهادی در مقایسه با سایر مکانیزم‌ها، تاخیر سرویس، هزینه و نقض تاخیر را به طور موثری کاهش می‌دهد.
Recent advancements in Internet of Things (IoT) technology have led to its widespread adoption across various domains, such as smart buildings, cities, and healthcare. Fog computing, as a distributed platform at the network edge, enables real-time execution of IoT applications. Due to the dynamic nature of fog environments and the continuously changing behavior of IoT applications, one of the key challenges is the optimal and dynamic provisioning of IoT services over available fog resources. This study aims to address this challenge by proposing a dynamic and optimal service provisioning mechanism using reinforcement learning techniques, such as learning automata. The proposed framework consists of a three-tier architecture (IoT, fog, and cloud layers) and a Dynamic Service Provisioning Manager (DSPM) component. The DSPM comprises three subcomponents: A service request and fog node status monitor, A workload analyzer, and A service provider, which collectively handle service provisioning at the fog layer. The mechanism is evaluated under three distinct scenarios: Analysis of real-world traffic flow, Comparison with optimal solutions, and The impact of service delay thresholds.Simulation results demonstrate that the proposed approach effectively reduces service delay, cost, and delay violation compared to existing mechanisms.
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