A kernel-based solution for overload in mixed criticality multicore systems

Mixed criticality systems are one of the relatively new directions of development for the classical real-time systems. As the real-time embedded systems become more and more complex, incorporating different tasks with different criticality levels, the continuous development of mixed criticality systems is only natural. These systems have practically entered every field where embedded systems are present: avionics, automotive, medical systems, wearable devices, home automation, industry and even the Internet of Things. While scheduling techniques have already been proposed in the literature for different types of mixed criticality systems, the number of papers addressing multiprocessor platforms running in a time-triggered mixed criticality environment is relatively low. These algorithms are easier to certify due to their complete determinism and isolation between components of different criticalities. Our research has centered on the problem of real-time scheduling on multiprocessor platforms for periodic tasks in a time-triggered mixed criticality environment. A partitioned, non-preemptive, table-driven scheduling algorithm was proposed, called Partitioned Time-Triggered Own Criticality Based Priority, based on a uniprocessor mixed criticality method. Furthermore, an analysis of the scheduling algorithm is provided in terms of success ratio by comparing it against an event-driven and a time-triggered method.

Safety-critical cyber-physical systems like autonomous cars require not only different levels of assurance, but also close interactions with dynamically-changing physical environments. While the former has been studied extensively by exploiting the notion of mixed-criticality (MC) systems, the latter has not, especially in conjunction with MC systems. To fill this important gap, we conduct an in-depth case study, demonstrating the importance of capturing current physical states, and introduce the problem of achieving efficient utilization of computing resources under varying physical states in MC systems. To solve this problem, we first develop a physical-state-aware MC task model, which is a generalization of the existing basic MC task model. We then propose new slack concepts tailored to the new task model, which enable efficient utilization of computing resources for MC systems. Finally, we develop a physical-state-aware dynamic slack management framework and demonstrate how to utilize the new MC task model and slack concepts towards efficient system utilization. We show, via a case study and in-depth evaluation, that the proposed framework makes 20x less low-criticality jobs dropped over a popular MC scheduling algorithm without compromising the MC-schedulability requirements.

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

In the field of real-time systems, component-based models for mixed criticality systems (MCS) on multicore platform are gaining significant attention of the researchers from the recent past. The concept of mixed-criticality is firstly applied on single core systems but after gaining attention, it evolved to multicore platform. Initially, the tasks of MCS executes in low mode however if a task having high criticality does not its scheduling requirements complete its low mode, then the system switch to high mode. In high mode, only high critical tasks executes its high mode scheduling requirements, while all low tasks are discarded from further execution. In the realm of MCS, the component-based model have high importance and has been used in large and complex real-time systems. In component-based model, the component of a system is splatted into multiple components. In this study, a new component-based scheduling approach is proposed for multicore MCS. In the multicore mixed-criticality compositional framework, special emphasis is given on deriving the supply bound function, which represents available core supply, and the demand bound function, which indicates the requirements of resources for scheduling. A mixed-criticality multicore compositional framework (MMCF) is introduced for component-based MCS having tightened deadline. For the proposed strategy, a schedulability analysis is conducted, ensuring that the system can be scheduled only if the supply bound function is equal to or less than the demand bound function. On a 4-core platform, the scheduleability for the three scheduling strategies i.e. MMCF, MC-Partition, and Global are shown at different utilization levels of resource. 100% schedulability is shown by the proposed MMCF, MC-Partition, and global techniques for target utilizations of 0.7, 0.5, and 0.4, respectively. For a given utilization U=0.6, the proposed MMCF approach shows 100 % schedulability, while the MC-partition and global approaches show 90% and 20% schedulabilities respectively. It shows that the proposed MMCF approach schedules 10% and 80% more task sets than the MC-partition and global approaches respectively. The experimental results reveal that the proposed MMCF approach offers better schedulability than the other two approaches.

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.

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.

Formal analysis of feature degradation in fault-tolerant automotive systems

An increasing trend in many safety-critical embedded system is to integrate multiple functional components into a shared hardware platform for better cost and power efficiency. This mixed criticality system offers challenges both for static schedulability analysis and run-time monitoring. In prior works, many papers studied the response-time analysis (RTA) scheduling of mixed criticality systems with dual-criticality levels. But in the real embedded systems, the criticality levels of the systems are always greater than two. In this paper we proposed a novel sufficient response-time analysis and optimal priority assignment scheme for fixed priority uniprocessor scheduling of mixed criticality system with arbitrary number of criticality levels. Evaluations illustrate the effectiveness of the scheme.

Embedded systems are pervasively used in many fields nowadays. In mixed-criticality environments (automotive, industry 4.0, drones, etc.) they need to run real-time applications with certain time and safety constraints alongside a rich operating system (OS). This is usually possible thanks to virtualization techniques, that leverage on hardware virtualization extensions on the machine. However, these hardware extensions might not cope with the security and safety requirements of the specific use case, and additionally, they might not always be available. A notable example is the emerging RISC-V architecture, that is today gaining a lot of traction in the mixed criticality field, but that does not offer today hardware virtualization extensions. In this paper VOSySmonitoRV is proposed as a mixed-criticality solution for RISC-V systems. VOSySmonitoRV allows the co-execution of two or more operating systems in a secure and isolated manner by running in the highest privileged machine level. A specific benchmark, measuring the interrupt latency and context switch time is done to assess the system performance in 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.

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.

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling approach is used for scheduling tasks on cores within clusters. The MC-Bfair scheduling algorithm reduces the number context switches and migration of tasks, which minimizes the overhead of mixed-criticality tasks. The migration and context switch overhead time is added at the time of each migration and context switch respectively for a task. In low critical mode, the low mode context switch and migration overhead time is added to task execution time, while the high mode overhead time of migration and context switch is added to the execution time of a task in high critical mode. The results obtained from experiments show the better schedulablity performance of proposed cluster-based technique as compared to cluster-based fixed priority (CB-FP), MC-EKG-VD-1, global and partitioned scheduling techniques e.g., for target utilization U=0.6, the proposed technique schedule 66.7% task sets while MC-EKG-VD-1, CB-FP, partitioned and global techniques schedule 50%, 33.3%, 16.7% and 0% task sets respectively.

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.

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.

Mixed-criticality (MC) system has attracted a lot of research attention in the past few years for its resource efficiency. Recent work attempted to provide a new MC model, the so-called Flexible Mixed-Criticality (FMC) task model, to relax the pessimistic assumptions in classic MC scheduling. However, in FMC, the behavior of MC tasks is still analyzed in offline stage. The run-time behavior such as dynamic slack has not yet been studied in FMC scheduling framework. In this paper, we present a utilization-based slack scheduling framework for FMC tasks. In particular, we monitor task execution on run time and collect dynamic slacks generated by task early completion. And these slacks can then be used either by high-criticality tasks to reduce mode-switches, or by low-criticality tasks so that less suspensions are triggered with more execution time, and thus quality of service is improved. We evaluate our approach with extensive simulations, and experiment results demonstrate the effectiveness of the proposed approaches.