The two-phase scheduling based on deep learning in the Internet of Things

Abstract

With the growth of data generation speed and its huge volume, the cloud-based infrastructure alone does not meet the needs of the Internet of Things (IoT), thereby leading to inefficiency. By combining fog and cloud computing on the IoT, some processing occurs near the data generation location with a higher speed and without needing a large bandwidth. Cloud and fog computing requires proper management in scheduling, increasing resource efficiency, and reducing consumption costs. In this article, a two-phase scheduling algorithm is presented based on deep learning methods in the field of IoT. The first phase is to decide on the location of the task execution using the clustering method. The second phase involves scheduling the task according to the execution location. In the clustering section, three ideas based on the Self-Organizing Map (SOM) clustering method have been proposed. In the first and second ideas, the SOM and hierarchical SOM are used to cluster the features of the tasks received from the IoT layer. In the third idea, the feature is extracted, and its dimensions are reduced using the Autoencoder, which is one of the deep learning methods, after which clustering is done. After scheduling each cluster's tasks, comparing the methods presented in the article, it is shown that the feature extraction using deep learning can improve clustering in such a way to reduce the missed rate of tasks in the cloud and fog, as well as their costs.