An intelligent multi- objective evolutionary schedulers to schedule realtime tasks for multicore architecture based automotive electronic control units [articol]

Abstract

In an automotive electronics applications there are approximately 230 electronic control units ECU’s are used to provide intelligent driving assistance. So, there is an effective multiple objective real time task scheduling techniques are required to provide better solution in this domain. This paper describes novel multiobjective 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 ECU. These techniques improve the performance upon earlier reported of an ECU’s by considering multiple objectives such as, low power consumption (P), maximizing core utilization (U) and minimizing deadline missrate (δ). This work also analysis 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 missrate ( ), 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 missrate is comparatively minimized with other scheduling approaches.