Preemptive uniprocessor scheduling of dual-criticality implicit-deadline sporadic tasks

Many reactive systems must be designed and analyzed prior to deployment in the presence of considerable epistemic uncertainty: the precise nature of the external environment the system will encounter, as well as the run-time behavior of the platform upon which it is implemented, cannot be predicted with complete certainty prior to deployment. The widely-studied Vestal model for mixed-criticality workloads addresses uncertainties in estimating the worst-case execution time (WCET) of real-time code. Different estimations, at different levels of assurance, are made about these WCET values; it is required that all functionalities execute correctly if the less conservative assumptions hold, while only the more critical functionalities are required to execute correctly in the (presumably less likely) event that the less conservative assumptions fail to hold but the more conservative assumptions do. A generalization of the Vestal model is considered here, in which a degraded (but non-zero) level of service is required for the less critical functionalities even in the event of only the more conservative assumptions holding. An algorithm is derived for scheduling dual-criticality implicit-deadline sporadic task systems specified in this more general model upon preemptive uniprocessor platforms, and proved to be speedup-optimal

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards

Towards a Fault-tolerant, Scheduling Methodology for Safety-critical Certified Information Systems

Today, many critical information systems have safety-critical and non-safety-critical functions executed on the same platform in order to reduce design and implementation costs. The set of safety-critical functionality is subject to certification requirements and the rest of the functionality does not need to be certified, or is certified to a lower level. The resulting mixed-criticality systems bring challenges in designing such systems, especially when the critical tasks are required to complete with a timing constraint. This paper studies a problem of scheduling a mixed-criticality system with fault tolerance. A fault-recovery technique called checkpointing is used where a program can go back to a recent checkpoint for re-execution upon errors occurred. A novel schedulability test is derived to ensure that the safety-critical tasks are completed before their deadlines and the theoretical correctness is shown

Improving the Schedulability and Quality of Service for Federated Scheduling of Parallel Mixed-Criticality Tasks on Multiprocessors

This paper presents federated scheduling algorithm, called MCFQ, for a set of parallel mixed-criticality tasks on multiprocessors. The main feature of MCFQ algorithm is that different alternatives to assign each high-utilization, high-critical task to the processors are computed. Given the different alternatives, we carefully select one alternative for each such task so that all the other tasks can be successfully assigned on the remaining processors. Such flexibility in choosing the right alternative has two benefits. First, it has higher likelihood to satisfy the total resource requirement of all the tasks while ensuring schedulability. Second, computational slack becomes available by intelligently selecting the alternative such that the total resource requirement of all the tasks is minimized. Such slack then can be used to improve the QoS of the system (i.e., never discard some low-critical tasks). Our experimental results using randomly-generated parallel mixed-critical tasksets show that MCFQ can schedule much higher number of tasksets and can improve the QoS of the system significantly in comparison to the state of the art

MCFlow: Middleware for Mixed-Criticality Distributed Real-Time Systems

Traditional fixed-priority scheduling analysis for periodic/sporadic task sets is based on the assumption that all tasks are equally critical to the correct operation of the system. Therefore, every task has to be schedulable under the scheduling policy, and estimates of tasks\u27 worst case execution times must be conservative in case a task runs longer than is usual. To address the significant under-utilization of a system\u27s resources under normal operating conditions that can arise from these assumptions, several \emph{mixed-criticality scheduling} approaches have been proposed. However, to date there has been no quantitative comparison of system schedulability or run-time overhead for the different approaches. In this dissertation, we present what is to our knowledge the first side-by-side implementation and evaluation of those approaches, for periodic and sporadic mixed-criticality tasks on uniprocessor or distributed systems, under a mixed-criticality scheduling model that is common to all these approaches. To make a fair evaluation of mixed-criticality scheduling, we also address some previously open issues and propose modifications to improve schedulability and correctness of particular approaches. To facilitate the development and evaluation of mixed-criticality applications, we have designed and developed a distributed real-time middleware, called MCFlow, for mixed-criticality end-to-end tasks running on multi-core platforms. The research presented in this dissertation provides the following contributions to the state of the art in real-time middleware: (1) an efficient component model through which dependent subtask graphs can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) support for optimizations to inter-component communication to reduce data copying without sacrificing the ability to execute subtasks in parallel; (3) a strict separation of timing and functional concerns so that they can be configured independently; (4) an event dispatching architecture that uses lock free algorithms where possible to reduce memory contention, CPU context switching, and priority inversion; and (5) empirical evaluations of MCFlow itself and of different mixed criticality scheduling approaches both with a single host and end-to-end across multiple hosts. The results of our evaluation show that in terms of basic distributed real-time behavior MCFlow performs comparably to the state of the art TAO real-time object request broker when only one core is used and outperforms TAO when multiple cores are involved. We also identify and categorize different use cases under which different mixed criticality scheduling approaches are preferable