تخصیص منبع آگاه از مهلت و انرژی با استفاده از ترکیب رویکرد حریصانه چندمعیاره و درخت تصمیم در محیط اینترنت اشیاء-مه– ابر

چکیده مقاله :

با رشد روزافزون اینترنت اشیا، حجم داده‌های جمع‌آوری‌شده از سنسورها به طور چشم‌گیری افزایش یافته است. با توجه به این امر، نیاز به اتصال اینترنت اشیا به سرورهای ابری برای رفع نیازهای ذخیره‌سازی، پردازش و تحلیل داده‌ها احساس می‌شود. همچنین ظهور فناوری میانی مانند مه، با انجام محاسبات اولیه بر روی درخواست‌ها در لبه شبکه، موجب کاهش حجم محاسبات ارسالی به ابر شده است. با این حال زمان‌بندی وظایف در منابع ابری، یک مسئله چالش‌برانگیز است. زمان‌بندی منابع به عنوان یک مسئله NP-Hard به معنای تخصیص و توزیع منابع (مانند پردازنده، حافظه، شبکه و ...) به وظایف ارسالی در سرورهای ابری به صورت بهینه و مؤثر می‌باشد. از این رو محققان زیادی سعی در ارائه روش‌های مبتنی بر الگوریتم‌های فراابتکاری برای یافتن راه‏حل‌های نزدیک به بهینه هستند. هدف اصلی در این روش‌ها یافتن منبع مناسب برای تخصیص به وظیفه است، حال آن که وضعیت وظیفه از نظر مهلت زمان اجرای وظیفه بر روی ماشین مجازی در نظر گرفته نمی‌شود. در کاربردهای اینترنت اشیا، داده‌ها ممکن است مربوط به وظایف بحرانی باشند که نیازمند پاسخ سریع هستند. به عبارت دیگر وظایفی که مهلت کمی برای اجرا دارند ممکن است در راستای بهبود سایر اهداف کیفیت سرویس به ماشین‌های مجازی با قدرت پردازشی کمتری ارسال شوند و در زمان مقرر قادر به اتمام نباشند که توجه زیادی به این مسئله در روش‌های پیشین نشده است. از این رو در این مقاله، رویکرد تخصیص منابع با استفاده از زمان‌بندی در بستر اینترنت اشیا- مه- ابر بر اساس ترکیب درخت تصمیم در راستای اولویت‏بندی وظایف و رویکرد حریصانه چندمعیاره ارائه شده است. نتایج شبیه‌سازی نشان می‏دهند روش پیشنهادی با تکیه بر اولویت‏بندی وظایف و ایجاد توازن در اهداف مختلف بر اساس رویکرد حریصانه چندمعیاره، با در نظر گرفتن فاکتورهای هزینه و زمان اتمام کار از نظر معیارهای ارزیابی نزدیک به بهینه عمل کرده و در مقایسه با روش‏های پیشین بهبود یافته است.

چکیده انگلیسی :

With the rapid growth of the Internet of Things (IoT), the volume of data collected from sensors has increased significantly. As a result, there is a growing need to connect IoT devices to cloud servers to meet the demands of data storage, processing, and analysis. Furthermore, the emergence of intermediate technologies, such as fog computing, which performs initial computations on requests at the network edge, has reduced the computational load sent to the cloud. However, task scheduling in cloud resources remains a challenging problem. Resource scheduling, as an NP-Hard problem, involves the optimal and efficient allocation and distribution of resources (such as processors, memory, networks, etc.) to tasks in cloud servers. Therefore, many researchers have attempted to propose heuristic-based algorithms to find near-optimal solutions. In these approaches, the primary goal is to find the appropriate resource for task allocation, while the task’s execution deadline is not always considered. In IoT applications, the data may correspond to critical tasks that require quick responses, which has often been overlooked in previous methods. Therefore, this paper proposes a resource allocation approach using scheduling in the IoT-Fog-Cloud framework, based on a combination of decision trees for task prioritization and a multi-criteria greedy approach. Simulation results show that the proposed method, by prioritizing tasks and balancing multiple objectives using a multi-criteria greedy approach, performs near-optimally in terms of evaluation criteria such as cost and task completion time, and improves upon previous methods.

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