High-criticality Mode Research Articles

Energy-Aware Adaptive Mixed-Criticality Scheduling with Semi-Clairvoyance and Graceful Degradation

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.

Energy-efficient scheduling of imprecise mixed-criticality real-time tasks based on genetic algorithm

Previous work focuses on the problem of energy for Imprecise Mixed-Criticality Systems on multiprocessor platforms, which provides degraded services for low criticality tasks in the high criticality mode. However, the proposed solution based on the partitioning heuristic Criticality-Unaware Worst Fit Decreasing method (CU-WFD) may consume more energy. In this paper, we propose a novel partitioning algorithm named EEAA based on the genetic algorithm and explore various parameter combinations, aiming to find a task-to-processor assignment method that can further reduce energy consumption with the best parameter combinations. In addition, we conduct experiments to evaluate EEAA and the experimental result shows that EEAA achieves energy savings of 12.56% compared to CU-WFD.

DVFS-based energy-aware scheduling of imprecise mixed-criticality real-time tasks

Energy-aware mixed-criticality (MC) real-time scheduling has attracted the attention of many researchers. However, they only focus on the conventional MC task model, in which all low-criticality (LO) tasks are discarded in the high-criticality (HI) mode. In this work, we focus on an imprecise mixed-criticality (IMC) task model that provides degraded service for LO tasks in HI mode. Firstly, we address the energy minimization problem of scheduling IMC task sets with dynamic voltage and frequency scaling (DVFS). Secondly, we present an energy-aware IMC scheduling algorithm (EA-IMC) to solve this problem while it schedules tasks with the energy-efficient speed of the LO mode SLO, and the maximum processor speed Smax in HI mode. Finally, the experiments are applied to evaluate the EA-IMC algorithm and the experimental results show that it can achieve on average 24.55% energy reduction compared with the existing algorithms.

Energy efficient non-preemptive scheduling of imprecise mixed-criticality real-time tasks

Previous studies focus on the classical mixed-criticality task model with preemptive dynamic priority scheduling in which all low-criticality (LO) tasks are dropped in the high-criticality mode (HI-mode). This paper focuses on the imprecise mixed-criticality (IMC) tasks model in which LO tasks reduce their execution budgets instead of being dropped. So far, the energy minimization problem of non-preemptive dynamic priority scheduling has received little attention. In this paper, we first develop schedulability tests of non-preemptive earliest deadline first with virtual deadline (NP-EDFVD) for IMC tasks, which is the first attempt for non-preemptive dynamic priority scheduling in single processor IMC systems. Subsequently, the uniform single speed (USS) algorithm based on the schedulability tests of NP-EDFVD is proposed to reduce energy consumption while protecting the IMC correctness. Finally, the experimental results show that the USS algorithm saves an average of 25.62% energy compared to NP-EDFVD.

Energy aware fixed priority scheduling in mixed-criticality systems

Most of studies about energy management for MC systems are based on dynamic priority scheme. The disadvantages of dynamic priority scheme are high system overhead and poor predictability. Unlike previous studies, we focus on the problem of scheduling mixed-criticality (MC) periodic tasks with minimizing energy consumption in MC systems based on fixed priority scheme. Firstly, we explain a criticality rate monotonic scheduling (CRMS) and propose the sufficient schedulability condition of CRMS. Secondly, we compute the energy minimization uniform scaled speed and present an optimal static solution algorithm based on CRMS. The extra workload of the high criticality level (HI) task executes with the maximum processor speed in the high criticality mode (HI-mode). But this algorithm does not exploit the slack time generated from the HI task in the low criticality mode (LO-mode). For energy efficiency, we propose a dynamic fixed priority energy minimization algorithm which exploits the slack time generated from the HI task in LO-mode to save energy. In addition, it combines a dynamic voltage and frequency scaling technique and a dynamic power management technique to reduce energy consumption. Finally, the experiments are applied to evaluate the performance of the proposed algorithm and the experimental results show that the proposed algorithm can save up 23.89% energy compared with other existing algorithms.

Energy aware algorithm based on actual utilization for periodic tasks in mixed-criticality real-time systems

Many real-time applications with different criticalities are integrated into a single mixed-criticality (MC) real-time system. Previous studies on MC systems assume that all tasks are executed in their worst case execution time and ignore the energy consumption in the high-criticality mode. In this paper, we focus on the actual execution time of tasks and consider the energy consumption in both the low-criticality mode and the high-criticality mode. First, we present a novel algorithm called EAU, which applies the actual execution time to re-compute the utilization of the task when a job is completed early or is released. In addition, it can apply the slack time generated from the early completion jobs and the jobs for which the processor speed is lower than the maximum processor speed in the high-criticality mode. Secondly, we analyze the scheduling feasibility of EAU. Finally, experiments are conducted to evaluate the performance of the proposed algorithm. The experimental results show that EAU can save up to 46.84% of energy compared with existing algorithms.

Toward the Design of Fault-Tolerance-Aware and Peak-Power-Aware Multicore Mixed-Criticality Systems

Mixed-Criticality (MC) systems have recently been devised to address the requirements of real-time systems in industrial applications, where the system runs tasks with different criticality levels on a single platform. In some workloads, a high-critically task might overrun and overload the system, or a fault can occur during the execution. However, these systems must be fault-tolerant and guarantee the correct execution of all high-criticality tasks by their deadlines to avoid catastrophic consequences, in any situation. Furthermore, in these MC systems, the peak power consumption of the system may increase, especially in an overload situation and exceed the processor Thermal Design Power (TDP) constraint. This may cause generating heat beyond the cooling capacity, resulting the system stop to avoid excessive heat and halting the processor. In this paper, we propose a technique for dependent dual-criticality tasks in fault-tolerant multi-core MC systems to manage peak power consumption and temperature. The technique develops a tree of possible task mapping and scheduling at design-time to cover all possible scenarios and reduce the low-criticality task drop rate in the high-criticality mode. At run-time, the system exploits the tree to select a proper schedule according to fault occurrences and criticality mode changes. Experimental results show that the average task schedulability is 74.14% on average for the proposed method, while the peak power consumption and maximum temperature are improved by 16.65% and 14.9 C on average, respectively, compared to a recent work. In addition, for a real-life application, our method reduces the peak power and maximum temperature by up to 20.06% and 5 C, respectively, compared to a state-of-the-art approach.

A novel view on bounding execution demand under mixed-criticality EDF

In this paper, we are concerned with scheduling a mix of high-criticality (HI) and low-criticality (LO) tasks under Earliest Deadline First (EDF) on one processor. To this end, the system implements two operation modes, LO and HI mode. In LO mode, HI tasks execute for no longer than their optimistic execution budgets and are scheduled together with the LO tasks. The system switches to HI mode, where all LO tasks are prevented from running, when one or more HI tasks run for longer than expected. Since these mode changes may happen at arbitrary points in time, it is difficult to find an accurate bound on carry-over jobs, i.e., those HI jobs that were released before, but did not finish executing at the point of the transition. To overcome this problem, we propose a technique that works around the computation of carry-over execution demand. Basically, the proposed technique separates the schedulability analysis of the transition between LO and HI mode from that of stable HI mode. We prove that a transition from LO to HI mode is feasible, if an equivalent task set derived from the original is schedulable under plain EDF. On this basis, we can apply approximation techniques such as, e.g., the well-known Devi’s test to derive further tests that trade off accuracy versus complexity/runtime. Finally, we perform a detailed comparison with respect to weighted schedulability on synthetic data illustrating benefits by the proposed technique.

Latency and Reliability Improvements of High Critical Tasks in Mixed Criticality Systems

Reliability and execution latency of high critical tasks are crucial for a successful execution in a mixed criticality system with tight design constraints. In this paper, we focus on two main design problems, namely latency-constrained maximum reliability problem and reliability-constrained minimum latency problem, for the applications running tasks of different criticality. The target architecture can run in two operating modes: low criticality mode (normal operating mode) and high criticality mode. For the former problem, we first find the minimum execution latency assuming the system runs in low criticality mode. Then, using this latency as lower bound, we present a heuristic algorithm to improve the reliability of high critical tasks in the application. The proposed algorithm assigns high critical tasks to the highest reliable processing elements in the technology library, and then, schedules low critical tasks without exceeding the given latency constraint. Similarly, for the latter problem, we first determine the highest reliability assuming the system runs in low criticality mode. Then, considering the overall system reliability, the proposed approach reduces the latest completion time of high critical tasks by giving them priority over low critical ones when selecting processing elements. The experimental evaluation conducted using task graphs shows up to 14.69% reliability improvement and 20.05%, on the average, latency improvement for the high critical tasks in the system.

FANTOM: Fault Tolerant Task-Drop Aware Scheduling for Mixed-Criticality Systems

Mixed-Criticality (MC) systems have emerged as an effective solution in various industries, where multiple tasks with various real-time and safety requirements (different levels of criticality) are integrated onto a common hardware platform. In these systems, a fault may occur due to different reasons, e.g., hardware defects, software errors or the arrival of unexpected events. In order to tolerate faults in MC systems, the re-execution technique is typically employed, which may lead to overrun of high-criticality tasks (HCTs), which necessitates the drop of low-criticality tasks (LCTs) or degrading their quality. However, frequent drops or relatively long execution times of LCTs (especially mission-critical tasks) are not always desirable and it may impose a negative impact on the performance, or the functionality of MC systems. In this regard, this article proposes a realistic MC task model and develops a design-time task-drop aware schedulability analysis based on the Earliest Deadline First with Virtual Deadline (EDF-VD) algorithm. According to this analysis and the proposed scheduling policy based on the new MC task model, in the high-criticality (HI) mode, when an HCT overruns and the system switches to the HI mode, the number of drops per LCT is prohibited from passing a predefined threshold. In addition, to guarantee the real-time constraints and safety requirements of MC tasks in the presence of faults (assuming transient faults in this article), a corresponding scheduling mechanism has been developed. According to the obtained results from an extensive set of simulations, which have been validated through a realistic avionic application, the proposed method improves the acceptance ratio by up to 43.9% compared to state-of-the-art.

Transmission scheduling for mixed-critical multi-user multiple-input and multiple-output industrial cyber-physical systems

Wireless sensor networks are widely used in industrial cyber-physical system installations, where high reliability and the need for real-time data are the two main characteristics. A large amount of real-time data can be transmitted to its destination on time using a reasonable periodic allocation of a node’s transmission slots. However, a flow may miss its deadline when flow conflicts occur. When such missed deadlines occur regularly, system performance may degrade, and when the flow is critical, such data losses can result in errors or cause disasters. To address this issue, we introduce multi-user multiple-input and multiple-output technology and a mixed-critical system into an industrial cyber-physical system. When an error occurs or when demand changes, the multi-user multiple-input and multiple-output nodes can switch their transmission mode, changing to a high-criticality configuration to meet the system’s new needs. Hence, we first propose a heterogeneous multi-user multiple-input and multiple-output system model. Based on this model, we propose a slot analyzing algorithm that guarantees system schedulability by reallocating slots for each node after replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By considering both system schedulability and cost, the slot analyzing algorithm also reduces the number of multi-user multiple-input and multiple-output nodes required. Then, to further reduce the number of multi-user multiple-input and multiple-output nodes in an industrial cyber-physical system, we propose a priority inversion algorithm that improves schedulability by adjusting slot allocations before replacing conflict nodes with multi-user multiple-input and multiple-output nodes. By reducing the use of multi-user multiple-input and multiple-output nodes, the priority inversion algorithm achieves better performance than the slot analyzing algorithm when the system is in a high-criticality mode. Evaluation results show the effectiveness and efficacy of our approaches.

Multi-rate fluid scheduling of mixed-criticality systems on multiprocessors

In this paper we consider the problem of mixed-criticality (MC) scheduling of implicit-deadline sporadic task systems on a homogenous multiprocessor platform. Focusing on dual-criticality systems, algorithms based on the fluid scheduling model have been proposed in the past. These algorithms use a dual-rate execution model for each high-criticality task depending on the system mode. Once the system switches to the high-criticality mode, the execution rates of such tasks are increased to meet their increased demand. Although these algorithms are speed-up optimal, they are unable to schedule several feasible dual-criticality task systems. This is because a single fixed execution rate for each high-criticality task after the mode switch is not efficient to handle the high variability in demand during the transition period immediately following the mode switch. This demand variability exists as long as the carry-over jobs of high-criticality tasks, that is jobs released before the mode switch, have not completed. Addressing this shortcoming, we propose a multi-rate fluid execution model for dual-criticality task systems in this paper. Under this model, high-criticality tasks are allocated varying execution rates in the transition period after the mode switch to efficiently handle the demand variability. We derive a sufficient schedulability test for the proposed model and show its dominance over the dual-rate fluid execution model. Further, we also present a speed-up optimal rate assignment strategy for the multi-rate model, and experimentally show that the proposed model outperforms all the existing MC scheduling algorithms with known speed-up bounds.

Cluster-based multicore real-time mixed-criticality scheduling

Abstract Cluster-based scheduling is recently gaining importance to be applied to mixed-criticality real-time systems on multicore processors platform. In this approach, the cores are grouped into clusters, and tasks that are partitioned among different clusters are scheduled by global scheduler in each cluster. This research work introduces a new cluster-based task allocation scheme for the mixed-criticality real-time task sets on multicore processors. For task allocation, smaller clusters sizes (sub-clusters) are used for mixed-criticality tasks in low criticality mode, while relatively larger cluster sizes are used for high criticality tasks in high criticality mode. In this research paper, the mixed-criticality task set is allocated to clusters using worst-fit heuristic. The tasks from each cluster are also allocated to its sub-clusters, using the same worst-fit heuristic. A fixed-priority response time analysis approach based on Audsley’s approach is used for the schedulability analysis of tasks in each cluster and sub-cluster. If the high criticality job is not completed after its worst case execution time in low mode, then the system is switched to high criticality mode. After mode switch, all the low criticalities tasks are discarded and only high criticality tasks are further executed in high criticality mode. Simulation results indicate that the percentage of schedulable task sets significantly increases under cluster scheduling as compared to partitioned and global mixed-criticality scheduling schemes.

Bounding the Demand of Mixed-Criticality Industrial Wireless Sensor Networks

Wireless sensor networks (WSNs) have been widely used in industrial systems. Industrial systems demand a high degree of reliability and real-time requirements in communications. In many industrial WSNs applications, flows with different levels of criticality coexist in the system. When errors or exceptions occur, high-criticality flows must be guaranteed reliably and in real time. However, only a few works focus on mixed-criticality industrial systems. Concerning this issue, in this paper, we study mixed-criticality industrial systems and propose a supply/demand bound function analysis approach based on earliest deadline first scheduling. In addition, our method considers both source routing and graph routing. At the beginning, when the system is in low-criticality mode, source routing considers the schedulability of each flow. When errors or exceptions occur, the system switches to high-criticality mode, and network routing turns to graph routing to guarantee that important flows can be scheduled. By estimating the demand bound for mixed-criticality systems, we can determine the schedulability of industrial systems. Experiments indicate the effectiveness and efficacy of our approach.

Scheduling Mixed-Criticality Real-Time Tasks in a Fault-Tolerant System

Enabling computer tasks with different levels of criticality running on a common hardware platform has been an increasingly important trend in the design of real-time and embedded systems. On such systems, a real-time task may exhibit different WCETs (Worst Case Execution Times) in different criticality modes. It is well-known that traditional real-time scheduling methods are not applicable to ensure the timely requirement of the mixed-criticality tasks. In this paper, the authors study a problem of scheduling real-time, mixed-criticality tasks with fault tolerance. An optimal, off-line algorithm is designed to guarantee the most tasks completing successfully when the system runs into the high-criticality mode. A formal proof of the optimality is given. Also, a novel on-line slack-reclaiming algorithm is proposed to recover from computing faults before the tasks' deadline during the run-time. Simulations show that an improvement of about 30% in performance is obtained by using the slack-reclaiming method.