AI based realtime task schedulers for multicore processor based low power biomedical devices for health care application

Abstract

The bioinformatics data processing plays a vital role in low power biomedical devices. The functional domain of processing biological data is collection, execution, conversion, storing and distribution. So, there is an effective multiple objective real time task scheduling technique are required to provide better solution in this domain. This paper describes novel AI based multi-objective evolutionary algorithmic techniques such as multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA) and multi-objective messy genetic algorithm (MOMGA) for scheduling real time tasks to a multicore processor-based low power biomedical device used for health care application. These techniques improve the performance upon earlier reported system by considering multiple objectives such as, low power consumption (P), maximizing core utilization (U) and minimizing deadline miss-rate (δ). The novelty of this work is to achieve the schedulability of realtime tasks by computing the converging value of a series of task parameters such as execution time, release time, workload and arrival time. Finally, we investigated the performance parameters such as power consumption (P), deadline miss-rate ($\updelta$), and core utilization for the given architecture. The evaluation results show that the power consumption is reduced to about 5–8%, utilization of the core is increased about 10% to 40% and deadline miss-rate is comparatively minimized with conventional realtime scheduling approaches.