A hybrid algorithm for task scheduling on heterogeneous multiprocessor embedded systems

Most of the scheduling algorithms proposed for real-time embedded systems, with energy constraints, try to reduce power consumption. However, reducing the power consumption may decrease the computation speed and impact the makespan. Therefore, for real-time embedded systems, makespan and power consumption need to be considered simultaneously. Since task scheduling is an NP-hard problem, most of the proposed scheduling algorithms are not able to find the multi-objective optimal solution. In this paper, we propose a two-phase hybrid task scheduling algorithm based on decomposition of the input task graph, by applying spectral partitioning. The proposed algorithm, called G-SP, assigns each part of the task graph to a low power processor in order to minimize power consumption. Through experiments, we compare the makespan and power consumption of the G-SP against well-known algorithms of this area for a large set of randomly generated and real-world task graphs with different characteristics. The obtained results show that the G-SP outperforms other algorithms in both metrics, under various conditions, involving different numbers of processors and considering several system configurations. (C) 2020 Published by Elsevier B.V.