New optimal solutions for real-Time scheduling of operating system tasks based on neural networks

Abstract

This paper work deals with the implementation of a neural networks based approach for real-Time scheduling of embedded systems composed by Operating Systems (OS) tasks in order to handle real-Time constraints in execution scenarios. In our approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-Time scheduling of embedded systems under resources constraints designed for real-Time applications running. Indeed, we present in this paper a new hybrid contribution that handles the real-Time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.