A Mixed-Criticality System (MCS) integrates multiple applications with different criticality levels on the same hardware platform. For power and energy-constrained systems such as Unmanned Aerial Vehicles, it is important to minimize energy consumption of the computing system while meeting reliability constraints. In this paper, we first determine the number of tolerated faults according to the given reliability target. Second, we propose a schedulability test for MCS with semi-clairvoyance and checkpointing. Third, we propose the Energy-Aware Scheduling with Reliability Constraint (EASRC) scheduling algorithm for MCS with semi-clairvoyance and checkpointing. It consists of an offline phase and an online phase. In the offline phase, we determine the offline processor speed by reclaiming static slack time. In the online phase, we adjust the processor speed by reclaiming dynamic slack time to further save energy. Finally, we show the performance of our proposed algorithm through experimental evaluations. The results show that the proposed algorithm can save an average of 9.67% of energy consumption compared with existing methods.

Both resource access protocols and real-time scheduling algorithms have been extensively studied in classic embedded real-time systems. However, there has been relatively little attention given to the resource access protocol and real-time scheduling algorithms in mixed-criticality systems. In this article, we pay attention to the problem of scheduling an imprecise mixed-criticality (IMC) taskset on a multiprocessor platform with shared resources. First, we propose an IMC with MSRP (IMC-MSRP) resource access protocol, which ensures mutually exclusive access to the shared resources for the tasks. Second, we propose the schedulability test based on the IMC-multiprocessor stack resource policy (MSRP) for a given task-to-processor mapping method. Third, we propose a feasible task-to-processor mapping algorithm called resource-aware criticality-unaware worst-fit decreasing (RA-CU-WFD), which first assigns tasks sharing the same resources to the same processor to reduce the global waiting time of the tasks and thus improve the schedulability ratio of the system. And then assigns tasks based on the criticality-unaware worst-fit decreasing (CU-WFD) algorithm. Finally, we conduct experiments using the synthetic tasksets, and the experimental results show that the RA-CU-WFD outperforms the other approaches in terms of the schedulability ratio.

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.

EDF-VD first focuses on the classic mixed-criticality task model in which all low-criticality (LO) tasks are abandoned in the high-criticality mode, which is an effective dynamic priority scheduling algorithm for mixed-criticality systems. However, it has low schedulability for the imprecise mixed-criticality (IMC) task model with constrained deadlines, in which LO tasks are provided graceful degradation services instead of being abandoned. In this article, we study how to improve schedulability for the IMC tasks model. First, we propose a novel criticalityaware EDF scheduling algorithm (CA-EDF) that tries to delay the LO task execution to improve schedulability. Second, we derive sufficient conditions of schedulability for CA-EDF based on the Demand Bound Function. Finally, we evaluate CA-EDF through extensive simulation. The experimental results indicate that CA-EDF can improve the schedulability ratio by about 13.10% compared to the existing algorithms.

Recent works introduce a semi-clairvoyant model, in which the system mode transition is revealed on the arrival of high-criticality jobs. To solve the problem of inconsistency between the correctness criterion for mixed-criticality systems (MCSs) with a semi-clairvoyant and the actual situation, we study the problem of schedulability and energy in MCS with the semi-clairvoyant model in this article. First, we propose a new correctness criterion for MCS with semi-clairvoyant and graceful degradation and develop the schedulability test based on demand bound function methods denoted as SCS-GD. Second, we propose an energy-efficient semi-clairvoyant scheduling algorithm based on SCS-GD denoted as EE-SCS-GD. Finally, we conduct an experimental evaluation of SCS-GD and EE-SCS-GD by synthetically generated task sets. The experimental results show that SCS-GD can improve the schedulability ratio by 5.98% compared to existing algorithms while EE-SCS-GD can save 56.17% energy compared to SCS-GD.

The classic Mixed-Criticality System (MCS) task model is a non-clairvoyance model in which the change of the system behavior is based on the completion of high-criticality tasks while dropping low-criticality tasks in high-criticality mode. In this paper, we simultaneously consider graceful degradation and semi-clairvoyance in MCS. We first propose the analysis for adaptive mixed-criticality with semi-clairvoyance denoted as C-AMC-sem. The so-called semi-clairvoyance refers to the system’s behavior change being revealed at the time that jobs are released. Moreover, we propose a new algorithm based on C-AMC-sem to reduce energy consumption. Finally, we verify the performance of the proposed algorithms via experiments upon synthetically generated tasksets. The experimental results indicate that the proposed algorithms significantly outperform 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.

Energy-aware real-time scheduling for mixed-criticality (MC) systems with different criticality levels has drawn many researchers’ attentions. However, most of the studies focus on the preemptive MC task model and few studies consider the nonpreemptive MC task model, in which all jobs cannot be preempted until completion. In this article, we address the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. First, we develop schedulability test of nonpreemptive earliest deadline first (NP-EDF) in single processor MC systems. Second, we extend the results to nonpreemptive earliest deadline first with virtual deadline (NP-EDFVD), which is the first attempt for nonpreemptive dynamic priority scheduling in single processor MC systems. Third, the energy-aware nonpreemptive scheduling algorithm (EANPS) based on NP-EDFVD is proposed to solve the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. Finally, an industrial use-case and extensive simulations are used to validate the performance of the proposed algorithm, and the experimental results show that the EANPS algorithm consumes average 25.72% less energy than that of NP-EDFVD.

The mixed-criticality system provides multiple real-time applications with different criticalities in a single system. Poor energy-saving performance of the previous studies on mixed-criticality sporadic tasks are mainly due to the fact that the slack time generated from the random arrival of sporadic tasks is not taken into account. In this article, we focus on scheduling energy aware mixed-criticality sporadic tasks and take the random arrival of sporadic tasks into account. We proposed a dynamically frequency updating mixed-criticality algorithm (DFU). DFU based on the earliest deadline first scheme can exploit the slack time generated from high criticality tasks in a low criticality mode to reduce processor frequency. In addition, it also can dynamically update the utilization of sporadic tasks set to further reduce processor frequency. The simulation experiments are conducted to evaluate the performance of DFU and experimental results show that DFU consumes 34.29% less energy than that of the existing algorithms.