زمانبندی وظایف در سیستمهای توزیع شده لایه مه و ابر محاسباتی با استفاده از الگوریتم بهینه‌سازی سوسک سرگین
محورهای موضوعی : فناوری اطلاعات
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
کلید واژه: اینترنت اشیاء, الگوریتم بهینه‌سازی سوسک سرگین, زمانبندی وظایف, لایه ابر, لایه مه,
چکیده مقاله :
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.
The Internet of Things has grown significantly in the past few years, and many intelligent objects have been connected to it. Cloud computing is a data processing system in the Internet of Things. However, the servers in the cloud computing paradigm are usually located at a long physical distance from the Internet of Things devices. The high latency caused by long distances cannot effectively implement real-time Internet of Things applications. Edge and fog computing has emerged as a popular computing technology in the field of the Internet of Things. One of the critical challenges of the Internet of Things is the problem of scheduling tasks in the fog and cloud layer. In the proposed method, the LSTM neural network allocates free resources, and the dung beetle optimization algorithm is used to schedule tasks optimally in the cloud and fog layer. Experiments show that in the HPC2N data set, the accuracy, sensitivity, and precision of the proposed method for predicting the state of resources are equal to 94.72%, 93.21%, and 91.64%, respectively. In the NASA data set, the proposed method's accuracy, sensitivity, and precision in resource allocation are 95.68%, 94.61%, and 92.37%, respectively. The proposed method is more accurate in allocating resources for scheduling than the RNN, 1DCNN, and MLP methods. The Makespan index of the proposed method shows a lower and better value in task scheduling than the AO_AVOA, AVOA, PSO, HHO, and FA methods.
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