Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems

Abstract

We consider partitioned scheduling of an Imprecise Mixed-Criticality (IMC) taskset on a uniform multiprocessor platform, with 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.