Low Criticality Research Articles

Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems

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.

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.

BOT-MICS: Bounding Time Using Analytics in Mixed-Criticality Systems

An increasing trend for reducing cost, space, and weight leads to modern embedded systems that execute multiple tasks with different criticality levels on a common hardware platform while guaranteeing a safe operation. In such mixed-criticality (MC) systems, multiple worst case execution times (WCETs) are defined for each task, corresponding to the system operation mode to improve the MC system’s timing behavior at runtime. Determining the appropriate WCETs for lower criticality (LC) modes is nontrivial. On the one hand, considering a very low WCET for tasks can improve the processor utilization by scheduling more tasks in that mode, on the other hand, using a larger WCET ensures that the mode switches (which causes by task overrunning) are minimized, thereby improving the quality of service for all tasks, albeit at the cost of processor utilization. Hitherto, no analytical solutions are proposed to determine WCETs in LC modes. In this regard, we propose a scheme to determine WCETs by the Chebyshev theorem, to make a tradeoff between the number of scheduled tasks at design-time and the number of dropped low-criticality tasks at runtime as a result of frequent mode switches. To have a tight bound of execution times and mode switching probability, we also propose a distribution analytics-based scheme, in which the mode switching probability is obtained based on the cumulative distribution function. Our experimental results show that our scheme improves the utilization of state-of-the-art MC systems by up to 72.27%, while maintaining 24.28% mode switching probability in the worst case scenario. Besides, the results of running embedded real-time benchmarks on a real platform show that the distribution-based scheme can improve the utilization by 7.30% while bounding the mode switching probability by 4.85% more, compared to the Chebyshev-based scheme.

Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems

In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works.

Task Migration and Scheduler for Mixed-Criticality Systems.

The interference between software components is increasing in safety-critical domains, such as autonomous driving. Low-criticality (LC) tasks, such as vehicle communication, may control high-criticality (HC) tasks, such as acceleration. In such cases, the LC task should also be considered as an HC task because the HC tasks relies on the LC task. However, the difficulty in guaranteeing these LC tasks is the catastrophic cost of computing resources, the electronic control unit in the domain of vehicles, required for every task. In this paper, we theoretically and practically provide safety-guaranteed and inexpensive scheduling for LC tasks by borrowing the computational power of neighbored systems in distributed systems, obviating the need for additional hardware components. As a result, our approach extended the schedulability of LC tasks without violating the HC tasks. Based on the deadline test, the compatibility of our approach with the task-level MC scheduler was higher than that of the system-level MC scheduler, such that the task-level had all dropped LC tasks recovered while the system-level only had 25.5% recovery. Conversely, from the worst-case measurement of violated HC tasks, the HC tasks were violated by the task-level MC scheduler more often than by the system-level MC scheduler, with 70.3% and 15.4% average response time overhead, respectively. In conclusion, under the condition that the HC task ratio has lower than 47% of the overall task systems at 80% of total utilization, the task-level approach with task migration has extensively higher sustainability on LC tasks.

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.

An Elastic Mixed-Criticality Task Model and Early-Release EDF Scheduling Algorithms

Many algorithms have recently been studied for scheduling mixed-criticality (MC) tasks. However, most existing MC scheduling algorithms guarantee the timely executions of high-criticality (HC) tasks at the expense of discarding low-criticality (LC) tasks, which can cause serious service interruption for such tasks. In this work, aiming at providing guaranteed services for LC tasks, we study an elastic mixed-criticality (E-MC) task model for dual-criticality systems. Specifically, the model allows each LC task to specify its maximum period (i.e., minimum service level) and a set of early-release points. We propose an early-release (ER) mechanism that enables LC tasks to be released more frequently and thus improve their service levels at runtime, with both conservative and aggressive approaches to exploiting system slack being considered, which is applied to both earliest deadline first (EDF) and preference-oriented earliest-deadline schedulers. We formally prove the correctness of the proposed early-release--earliest deadline first scheduler on guaranteeing the timeliness of all tasks through judicious management of the early releases of LC tasks. The proposed model and schedulers are evaluated through extensive simulations. The results show that by moderately relaxing the service requirements of LC tasks in MC task sets (i.e., by having LC tasks’ maximum periods in the E-MC model be two to three times their desired MC periods), most transformed E-MC task sets can be successfully scheduled without sacrificing the timeliness of HC tasks. Moreover, with the proposed ER mechanism, the runtime performance of tasks (e.g., execution frequencies of LC tasks, response times, and jitters of HC tasks) can be significantly improved under the ER schedulers when compared to that of the state-of-the-art earliest deadline first—virtual deadline scheduler.