زمانبندی وظایف در سیستمهای توزیع شده لایه مه و ابر محاسباتی با استفاده از الگوریتم بهینه‌سازی سوسک سرگین
الموضوعات :
1 - دانشجوی دکتری مهندسی کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
2 - استادیار گروه کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
الکلمات المفتاحية: اینترنت اشیاء, الگوریتم بهینه‌سازی سوسک سرگین, زمانبندی وظایف, لایه ابر, لایه مه,
ملخص المقالة :
در چند سال گذشته اینترنت اشیاء رشد قابل توجه‌ای داشته است و تعداد زیادی شی هوشمند به آن متصل شده است. رایانش ابری به عنوان یک سیستم پردازش داده‌ها در اینترنت اشیاء است با این حال، سرورها در الگوی محاسبات ابری معمولاً در یک فاصله فیزیکی طولانی از دستگاه‌های اینترنت اشیاء قرار دارند و تأخیر زیاد ناشی از فواصل طولانی نمی‌تواند به طور مؤثر برنامه‌های اینترنت اشیاء بلادرنگ را برآورده کند. به دلیل این مسائل، محاسبات لبه و مه به عنوان فناوری محاسباتی محبوب در زمینه اینترنت اشیا ظاهر شده است. یکی از چالش‌های مهم اینترنت اشیاء، مسئله زمانبندی وظایف در لایه مه و ابر است. در روش پیشنهادی برای تخصیص منابع آزاد از شبکه عصبی LSTM استفاده می‌شود و برای زمانبندی بهینه وظایف در لایه ابر و مه از الگوریتم بهینه‌سازی سوسک سرگین استفاده می‌شود. آزمایشات نشان می‌دهد که در مجموعه داده HPC2N دقت، حساسیت و صحت روش پیشنهادی برای پیش بینی وضعیت منابع به ترتیب برابر 94/72 درصد، 93/21 درصد و 91/64 درصد است. در مجموعه داده NASA دقت، حساسیت و صحت پیش بینی روش پیشنهادی در تخصیص منابع به ترتیب برابر 95/68 درصد، 94/61 درصد و 92/37 درصد است. روش پیشنهادی نسبت به روشهای RNN، 1DCNN، MLP دقت بیشتری در تخصیص منابع برای زمانبندی دارد. شاخص Makespan روش پیشنهادی نسبت به روشهای AO_AVOA، AVOA، PSO، HHO و FA مقدار کمتری و بهتری را در زمانبندی وظایف نشان می‌دهد.
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