زمان‌بندی مبتنی بر اولویت وظایف با استفاده از سیستم فازی در محاسبات لبه سیار
محورهای موضوعی : مهندسی برق و کامپیوتر
انتصار حسینی
1
(دانشگاه قم)
محسن نیک رای
2
(دانشگاه قم)
شمس اله قنبری
3
(دانشگاه آزاد واحد آشتیان)
کلید واژه: محاسبات لبه سیار, زمان‌بندی, حریصانه, فازی, انرژی مصرفی, زمان انتظار,
چکیده مقاله :
محاسبات لبه سیار، تکنولوژی نوینی برای بهبود مشکل تأخیر، ظرفیت و منابع موجود در محیط محاسبات ابری سیار است. هدف اصلی در محاسبات لبه سیار، زمان‌بندی پویا و بارگذاری بهینه با کمترین هزینه در استفاده از منابع است. ما در این مقاله، از یک مدل سیستم سه‌سطحی دستگاه‌های سیار، لبه و ابر استاندارد، استفاده و دو الگوریتم بارگذاری و زمان‌بندی را پیشنهاد می‌کنیم. یک الگوریتم تصمیم‌گیری برای بارگذاری وظایف مبتنی بر الگوریتم کوله‌پشتی حریصانه در سمت دستگاه سیار است که وظایف با انرژی مصرفی بالا را برای بارگذاری انتخاب می‌کند و باعث صرفه‌جویی در انرژی مصرفی دستگاه می‌شود. همچنین در سمت MEC، یک الگوریتم زمان‌بندی پویا را با اولویت‌بندی وظایف مبتنی بر فازی جهت اولویت‌بندی و زمان‌بندی وظایف بر اساس دو معیار ارائه می‌کنیم. نتایج عددی نشان می‌دهند که کار ارائه‌شده در مقایسه با سایر روش‌ها باعث کاهش زمان انتظار وظایف برای اجرا، تأخیر و بار سیستم می‌شود و تعادل سیستم با کمترین تعداد منابع تأمین می‌گردد و سیستم ارائه‌شده، مصرف باتری را در دستگاه هوشمند تا حدود 90% کاهش می‌دهد. نتایج نشان می‌دهند که بیش از 92% وظایف با موفقیت در محیط لبه اجرا می‌شوند.
Mobile edge computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are important issue in MEC. In this paper, we use a standard three-level system model of mobile devices, edge and cloud, and propose two offloading and scheduling algorithms. A decision-making algorithm for offloading tasks is based on the greedy Knapsack offloading algorithm (GKOA) on the mobile device side, which selects tasks with high power consumption for offloading and it saves energy consumption of the device. On the MEC side, we also present a dynamic scheduling algorithm with fuzzy-based priority task scheduling (FPTS) for prioritizing and scheduling tasks based on two criteria. Numerical results show that our proposed work compared to other methods and reduces the waiting time, latency and system overhead. Also, provides the balance of the system with the least number of resources. And the proposed system reduces battery consumption in the smart device by up to 90%. The results show that more than 92% of tasks are executed successfully in the edge environment.
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