Partitioned mixed-criticality scheduling on multiprocessor platforms

Abstract

Scheduling mixed-criticality systems that integrate multiple functionalities with different criticality levels into a shared platform appears to be a challenging problem, even on single-processor platforms. Multi-core processors are more and more widely used in embedded systems, which provide great computing capacities for such mixed-criticality systems. In this paper, we propose a partitioned scheduling algorithm MPVD to extend the state-of-the-art single-processor mixed-criticality scheduling algorithm EY to multiprocessor platforms. The key idea of MPVD is to evenly allocate tasks with different criticality levels to different processors, in order to better explore the asymmetry between different criticality levels and improve the system schedulability. Then we propose two enhancements to further improve the schedulability of MPVD. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over existing algorithms.