An adaptive approach based on resource-awareness towards power-efficient real-time periodic task modeling on embedded IoT devices

Abstract

Embedded devices are gaining popularity day by day due to the expanded use of Internet of Things applications. However, these embedded devices have limited capabilities concerning power and memory. Thus, the applications need to be tailored in such a way to perform the specified tasks within the constrained resources with the same accuracy. In Real-Time task scheduling, one of the challenging factors is the intelligent modelling of input tasks in such a way that it produces not only logically correct output within the deadline but also consumes minimum CPU power. Algorithms like Rate Monotonic and Earliest Deadline First compute hyper-period of input tasks for periodic repetition of the same set of tasks on CPU. However, at times when the tasks are not adequately modelled, they lead to an enormously high value of hyper-period which result in more CPU cycles and power consumption. Many state-of-the-art solutions are presented in this regard, but the main problem is that they limit tasks from having all possible period values; however, with the vision of Industry 4.0, where most of the tasks will be doing some critical manufacturing activities, it is highly discouraged to prevent them of a certain period. In this paper, we present a resource-aware approach to minimise the hyper-period of input tasks based on device profiles and allows tasks of every possible period value to admit. The proposed work is compared with similar existing techniques, and results indicate significant improvements regarding power consumptions.