Resource Synchronization and Preemption Thresholds Within Mixed-Criticality Scheduling

In the last decade, the rapid evolution of the Commercial-Off-The-Shelf (COTS) platforms led safety-critical systems towards integrating tasks and applications with different criticality levels in a shared hardware platform, i.e., Mixed-Criticality Systems (MCS)s. Therefore, several scheduling algorithms and approaches have been proposed upon a commonly used model, i.e., Vestal's model. However, consolidating software functions onto shared processors cannot be implemented directly in real-life applications and industrial systems while complying with certification requirements. The existing scheduling approaches do not provide a simple solution for eliminating the interference effect among the tasks with different criticality levels on the shared processing resources. Moreover, the system mode switch guarantees the timing constraints of the high-criticality tasks throw the termination of the low-criticality tasks. In this paper, we developed a new scheduling algorithm that addresses these challenges based on the round-robin technique, which improves the overall schedulability. We compared the proposed algorithm against existing scheduling algorithms in both academia and industry using extensive experiments to evaluate it. Our results show improvements in the schedulability from 0.8% to 14.0% and from 2.7% to 10.7% compared to the conventional Earliest Deadline First with Virtual Deadline (EDF-VD) and Fixed Priority Preemptive (FPP) scheduling approaches, respectively.

Mixed-Criticality Scheduling (MCS) is an effective approach to addressing diverse certification requirements of safety-critical systems that integrate multiple subsystems with different levels of criticality. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption, ranging from fully-preemptive to fully-non-preemptive scheduling. We present schedulability analysis algorithms to enable integration of PTS with MCS, in order to bring the rich benefits of PTS into MCS, including minimizing the application stack space requirement, reducing the number of runtime task preemptions, and improving schedulability.

REALISM: Reliability-aware energy management in multi-level mixed-criticality systems with service level degradation

Systems in many safety-critical application domains are subject to certification requirements. For any given system, however, it may be the case that only a subset of its functionality is safety-critical and hence subject to certification; the rest of the functionality is non-safety-critical and does not need to be certified, or is certified to lower levels of assurance. The certification-cognizant runtime scheduling of such mixed-criticality systems is considered. An algorithm called EDF-VD (for Earliest Deadline First with Virtual Deadlines) is presented: this algorithm can schedule systems for which any number of criticality levels are defined. Efficient implementations of EDF-VD, as well as associated schedulability tests for determining whether a task system can be correctly scheduled using EDF-VD, are presented. For up to 13 criticality levels, analyses of EDF-VD, based on metrics such as processor speedup factor and utilization bounds, are derived, and conditions under which EDF-VD is optimal with respect to these metrics are identified. Finally, two extensions of EDF-VD are discussed that enhance its applicability. The extensions are aimed at scheduling a wider range of task sets, while preserving the favorable worst-case resource usage guarantees of the basic algorithm.

A common trend in real-time embedded systems is to integrate multiple applications on a single platform. Such systems are known as mixed-criticality (MC) systems when the applications are characterized by different criticality levels. Nowadays, multicore platforms are promoted due to cost and performance benefits. However, certification of multicore MC systems is challenging as concurrently executed applications of different criticalities may block each other when accessing shared platform resources. Most of the existing research on multicore MC scheduling ignores the effects of resource sharing on the response times of applications. Recently, a MC scheduling strategy was proposed, which explicitly accounts for these effects. This paper discusses how to combine this policy with an optimization method for the partitioning of tasks to cores as well as the static mapping of memory blocks, i.e., task data and communication buffers, to the banks of a shared memory architecture. Optimization is performed at design time targeting at minimizing the worst-case response times of tasks and achieving efficient resource utilization. The proposed optimization method is evaluated using an industrial application.

Efficient schedulability analysis for mixed-criticality systems under deadline-based scheduling

A Mixed-Criticality System (MCS) features the integration of multiple subsystems that are subject to different levels of safety certification on a shared hardware platform. In cost-sensitive application domains such as automotive E/E systems, it is important to reduce application memory footprint, since such a reduction may enable the adoption of a cheaper microprocessor in the family. Preemption Threshold Scheduling (PTS) is a well-known technique for reducing system stack usage. We consider partitioned multiprocessor scheduling, with Preemption Threshold Adaptive Mixed-Criticality (PT-AMC) as the task scheduling algorithm on each processor and address the optimization problem of finding a feasible task-to-processor mapping with minimum total system stack usage on a resource-constrained multi-processor. We present the Extended Maximal Preemption Threshold Assignment Algorithm (EMPTAA), with dual purposes of improving the taskset’s schedulability if it is not already schedulable, and minimizing system stack usage of the schedulable taskset. We present efficient heuristic algorithms for finding sub-optimal yet high-quality solutions, including Maximum Utilization Difference based Partitioning (MUDP) and MUDP with Backtrack Mapping (MUDP-BM), as well as a Branch-and-Bound (BnB) algorithm for finding the optimal solution. Performance evaluation with synthetic task sets demonstrates the effectiveness and efficiency of the proposed algorithms.

This paper presents a design and implementation of a Mixed-Criticality System (MCS) extended fromμC/OS III. It is based on a MCS model that uses an adaptive reservation mechanism to cope with the uncertainties in task execution times and to increase the resource utilization in MCS. The implementation takes advantage of the tasks’ recent execution records to predict their required computational resource in the near future and adjusts their reserved budget according to their criticality levels. The designed system focuses on soft real-time tasks. An overrun tolerance algorithm is used to limit the deadline miss ratios between a rise to the task’s actual consumption and the change to the amount of reservation. More than two criticality levels can be handled without introducing obvious additional overhead at each added level. The case study evaluation demonstrates that the reserved resource for each task is always close to its actual consumption; the tasks’ deadline misses are bounded by the different requirements specified by the criticality levels; during overload conditions, high-criticality tasks are guaranteed to have sufficient resource reservation. Although there is still room for improvement if it comes to processing overhead, this research brings some inspirations in both modelling and implementation aspects of MCS.

In some cases, tasks may be allowed to migrate from one processor to another, e.g., Due to hardware failures or for workload balancing. If a mixed-criticality setting is considered, it is necessary to decide whether new tasks with different levels of criticality may be accepted by a processor without compromising the already running tasks. Since this decision has to be taken on-line, there is a need for fast but yet accurate schedulability tests for mixed-criticality systems. In this paper, we consider that the EDF-VD algorithm is used to schedule tasks on the different processors. EDF-VD assigns virtual deadlines to high-criticality tasks, i.e., It uniformly downscales their real deadlines, to account for a potential increase in their execution demand. A deadline scaling factor is hence computed for the whole processor. However, in the case where the increase in computation demand strongly differs from one task to another, scaling deadlines uniformly makes EDF-VD incur pessimism. Of course, a scaling factor can be computed for every single task, however, this leads to a considerably more complex algorithm which cannot be used in an on-line setting. As a result, we propose an intermediate solution by introducing a bi-level deadline scaling. This way, high-criticality tasks that experience a small increase of workload are assigned one scaling factor, whereas tasks with a large increase of workload are assigned a separate scaling factor. Our experiments show that the proposed approach dominates the original EDF-VD algorithm while it does not increase complexity allowing for constant-time admission control in mixed-criticality systems.

Cluster-based multicore real-time mixed-criticality scheduling

The traditional mixed-criticality (MC) model does not allow less critical tasks to execute during an event of the error and exception. Recently, the imprecise MC (IMC) model has been proposed where, even for exceptional events, less critical tasks also receive some amount of (degraded) service, e.g., a task overruns its execution demand. In this work, we present our ongoing effort to extend the IMC model to the precise scheduling of tasks and integrate with the dynamic voltage and frequency scaling (DVFS) scheme to enable energy minimization. Precise scheduling of MC systems is highly challenging because of its requirement to simultaneously guarantee the timing correctness of all tasks under both pessimistic and less pessimistic assumptions. We propose an utilization-based schedulability test and sufficient schedulability conditions for such systems under earliest deadline first with virtual deadline (EDF-VD) scheduling policy. For this unified model, we present a quantitative study in the forms of speedup bound and approximation ratio. Finally, both theoretical and experimental analysis will be conducted to prove the correctness of our algorithm and to demonstrate its effectiveness.

The current trend in embedded systems towards integrating multiple functionalities with different degrees of criticalities on a shared computing platform. The concept of mixed-criticality system has been presented and is becoming common in embedded systems. The scheduling problem for mixed-criticality systems has become a hot research topic. A algorithm called EDF-VD was proposed for mixed- criticality systems, it can make a better performance guarantee. In order to increase the number of schedulable tasks, an improved mixed-criticality scheduling algorithm based on EDF-VD is proposed. In the proposed algorithm, a probabilistic parameter is introduced to represent the probability that the execution time of high criticality task exceeding the worst execution time at its low criticality mode, and sufficient conditions that the mixed-criticality task instances can be scheduled are derived. Simulation results show that the number of schedulable tasks in the proposed algorithm is more than the EDF-VD algorithm.

Mixing tasks of different criticality levels on the same processor has become common in modern embedded systems. Scheduling these complex systems on multicore processors is difficult, especially when targeting high utilization. The overload in mixed criticality systems is one of the important problems that faces system designers and may force them to lower the cores utilization to avoid this risky problem. If a fully utilized system is overloaded and no solution is applied, many of coming jobs will miss their deadlines and the system may stay overloaded all the time and never return back to its normal behavior. So, the overload must be transient and the system should guarantee that it can return back its normal behavior shortly after overload happens. In this paper, we present a kernel-based solution for the overload in mixed criticality systems using virtual time. A virtual timer delays the release time and deadlines of tasks in an overload state for a short time to guarantee that it can return back as quickly as possible to its normal behavior. The proposed solution avoids using any user space invocation, as it detects the overload and recovers from it completely inside the kernel space. Our experiments show that the time to recover from overload once it happens is decreased using our proposed technique.

Motivated by the increasing trend in embedded systems towards platform integration, there has been an increasing research interest in scheduling mixed-criticality systems. However, most existing efforts have concentrated on scheduling sequential tasks and ignored intra-task parallelism. In this paper, we study the scheduling of mixed-criticality parallel jobs on multiprocessor platforms. We propose a synchronous mixed-criticality job model, where each job consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. A novel MinLoad algorithm is developed to decompose mixed-criticality parallel jobs into mixed-criticality sequential jobs. This decomposition enables us to leverage existing mixed-criticality scheduling algorithms and schedulability analysis to the multiprocessor scheduling of mixed-criticality parallel jobs. In addition, our MinLoad job decomposition algorithm is designed to make the decomposed mixed-criticality sequential tasks easier to schedule, and thus requires smaller-sized multiprocessor platforms for the mixed-criticality systems.

Multi-core architectures have grown to be a popular choice for deploying Mixed Criticality Systems (MCS). The focus of research in MCS has been to provide timing assurances for jobs with different criticality levels. Due to their significant processing demands and energy-aware/constrained nature, energy conservation in these systems is becoming mandatory. This article presents, mcDVFS, an energy management technique based on Dynamic-Voltage-and-Frequency-Scaling for multi-core MCS. mcDVFS achieves significant energy reduction while maintaining timing guarantees. It also prioritizes quality of service whenever feasible. Extensive experimental simulations show energy savings of ≈ 52% and 34% when compared to EDF-VD and EDF-VD with QoS.