Shared resources and energy consumption are important factors to consider in the design of mixed-criticality systems. Existing works have studied these two factors separately. In this article, we simultaneously focus on shared resources and energy consumption on multiprocessor platforms. Firstly, we address the problem of energy-aware scheduling for the fixed-priority imprecise mixed-criticality tasks with shared resources and propose a schedulability test based on the Multiprocessor Priority Ceiling Protocol for a given task-to-processor mapping. Secondly, we calculate the energy-efficient speed of each processor based on the schedulability test and propose the corresponding task-to-processor mapping algorithm, called IMCPA. Finally, we conduct experiments on a real-world case and synthetic tasksets. The experimental results show that IMCPA can improve the schedulability ratio by about 13.76% and save energy consumption by about 34.89% compared to the existing algorithms.

We consider partitioned scheduling of an imprecise mixed-criticality (IMC) taskset on a uniform multiprocessor platform, with the earliest deadline first-virtual deadline (EDF-VD) as the uniprocessor task scheduling algorithm, and address the optimization problem of finding a feasible task-to-processor assignment and low-criticality (LO) mode processor speed with the objective of minimizing the system’s average energy consumption in LO mode. We propose a task-to-processor assignment algorithm criticality-unaware worst-fit decreasing (CU-WFD) algorithm, which allocates tasks with the worst-fit decreasing (WFD) heuristic method based on utilization values at their respective criticality levels. We determine the energy-efficient speed for each processor based on EDF-VD scheduling, and present our algorithm energy-efficient partitioned scheduling for imprecise mixed-criticality (EEPSIMC) with the CU-WFD heuristic algorithm to minimize system energy consumption. The experimental results show that our proposed algorithm has good performance in terms both schedulability ratio and normalized energy consumption compared to seven comparison baselines.