زمان‌بندی ماژول‌ها در محاسبات مه به روش جستجوی هم‌زیستی جانداران مبتنی بر کوله‌پشتی
محورهای موضوعی : مهندسی برق و کامپیوتردادمهر رهبری 1 , محسن نیک‌رای 2
1 - دانشگاه قم
2 - دانشگاه قم
کلید واژه: الگوریتم هم‌زیستی جانداران زمان‌بندی کوله‌پشتی محاسبات مه.,
چکیده مقاله :
شبکه‌های حسگر بی‌سیم دارای محدودیت‌هایی از قبیل توان پردازشی، منابع ذخیره‌سازی و تأخیر زمانی در انتقال داده‌ها به ابر می‌باشند. محاسبات مه به وسیله توسعه سرویس‌های ابری به لبه شبکه موجب کاهش ترافیک و تأخیر زمانی می‌شود و بنابراین این نوع شبکه‌ها در سیستم‌های بسیاری مانند مراقبت پزشکی، ابزارهای پوشیدنی، سیستم حمل و نقل و شهرهای هوشمند کاربرد دارد. تکنیک‌های زمان‌بندی وظایف در محاسبات مه از جمله مسایل NP-hard محسوب می‌شود. برنامه‌ها جهت اجراشدن به منابع نیاز دارند. ابزارهای لبه شبکه به حسگرها و ابر نزدیک بوده و دارای قدرت پردازشی لازم برای اجرای برنامه‌ها می‌باشند. هر ابزار لبه می‌تواند برای پیاده‌سازی سیاست‌های تخصیص منابع مورد استفاده قرار گیرد. در این مقاله، ما با ارائه یک روش مبتنی بر کوله‌پشتی بهینه‌شده با الگوریتم هم‌زیستی جانداران به تخصیص مناسب منابع به وظایف در شبکه‌های مه می‌پردازیم. روش پیشنهادی در شبیه‌ساز iFogsim به عنوان یک کتابخانه توسعه‌یافته از کلودسیم جهت پردازش مه پیاده‌سازی شده است. نتایج نشان‌دهنده بهبود در انرژی مصرفی، مصرف منابع و هزینه اجرای شبکه می‌باشد که روش پیشنهادی بهتر از روش کوله‌پشتی و الگوریتم پردازش به ترتیب ورود عمل نموده است.
Wireless sensor networks have limitations such as processing power, storage resources, and time delay in data transfer to the cloud. The cloud computing by the development of cloud-based services to the edge of the network reduces traffic and delays, so these types of networks are used in many systems, such as medical care, wearable devices, transportation systems and smart cities. Task scheduling techniques in fog computing are considered to be NP-hard issues. Applications require resources to run. Network fog devices are close to the sensors and the cloud and have the required processing power to run the applications. Each fog device can be used to run resource allocation policies. In this paper, we present an optimized Knapsack-based method optimized by symbiotic organism search to allocate resources appropriately to tasks in fog network. The proposed method is simulated in the iFogsim as a developed library from Cloudsim for fog computing. The results indicate improvement in energy consumption, resource consumption, and execution cost of the network. The proposed method is better than FCFS and Knapsack methods.
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