Design of a model based engineering deep learning scheduler in cloud computing environment using Industrial Internet of Things (IIOT)

Abstract

The Industrial Internet of Things (IIoT) integrated with cloud computing resources offers effective advancements in the field of industrial automation with open connectivity and emergent computing. With such advancements, the supplementary services evolves in order to augment the automation process in manufacturing and production industries. However, the integration and transformation of cloud and IIoT environment poses serious limitation on reliability and computational performance. It is very necessary to develop a framework that handles and provides a bridge between the rapid input acquisitions from an IIoT device and rapid data processing or storage by the cloud. In this paper, model based engineering (MBE) model is used to control the workflow in cloud environment, which is integrated with a deep learning algorithm named recurrent neural network (RNN) algorithm. The RNN is utilized to control the future workload prediction and supply necessary resources to balance the workflow in cloud computing environment. The entire predictions are carried out based on the input data acquisition rate from the IIoT devices. The storage or processing of the input data is carried out by the Virtual Machines presents in cloud environment. The resources allocation and scheduling of workload is carried out in the control plane using RNN that optimizes the workflow. The RNN is resumed to solve iteratively the task of optimization. The experiments are conducted to test the efficacy of the model and the results are verified in terms of total energy consumption, task response time, and VM Utilization. The experiments are conducted with same/different types of IIoT devices. The simulation results reveal the improved efficacy of the MBE–RNN model than existing deep learning and MBE schedulers. The results show that the MBE–RNN acquires improved processing ability with reduced task response time and energy utilization.