Schedulability Analysis Of General Task Model And Demand Aware Scheduling In Mixed Criticality Systems

Schedulability Analysis of General Task Model and Demand Aware Scheduling in Mixed-criticality Systems

Schedulability in Mixed-criticality Systems

Real-time safety-critical systems must complete their tasks within a given time limit. Failure to successfully perform their operations, or missing a deadline, can have severe consequences such as destruction of property and/or loss of life. Examples of such systems include automotive systems, drones and avionics among others. Safety guarantees must be provided before these systems can be deemed usable. This is usually done through certification performed by a certification authority.Safety evaluation and certification are complicated and costly even for smaller systems.One answer to these difficulties is the isolation of the critical functionality. Executing tasks of different criticalities on separate platforms prevents non-critical tasks from interfering with critical ones, provides a higher guaranty of safety and simplifies the certification process limiting it to only the critical functions. But this separation, in turn, introduces undesirable results portrayed by an inefficient resource utilization, an increase in the cost, weight, size and energy consumption which can put a system in a competitive disadvantage.To overcome the drawbacks of isolation, Mixed Criticality (MC) systems can be used. These systems allow functionalities with different criticalities to execute on the same platform. In 2007, Vestal proposed a model to represent MC-systems where tasks have multiple Worst Case Execution Times (WCETs), one for each criticality level. In addition, correctness conditions for scheduling policies were formally defined, allowing lower criticality jobs to miss deadlines or be even dropped in cases of failure or emergency situations.The introduction of multiple WCETs and different conditions for correctness increased the difficulty of the scheduling problem for MC-systems. Conventional scheduling policies and schedulability tests proved inadequate and the need for new algorithms arose. Since then, a lot of work has been done in this field.In this thesis, we contribute to the study of schedulability in MC-systems. The workload of a system is represented as a set of jobs that can describe the execution over the hyper-period of tasks or over a duration in time. This model allows us to study the viability of simulation-based correctness tests in MC-systems. We show that simulation tests can still be used in mixed-criticality systems, but in this case, the schedulability of the worst case scenario is no longer sufficient to guarantee the schedulability of the system even for the fixed priority scheduling case. We show that scheduling policies are not predictable in general, and define the concept of weak-predictability for MC-systems. We prove that a specific class of fixed priority policies are weakly predictable and propose two simulation-based correctness tests that work for weakly-predictable policies.We also demonstrate that contrary to what was believed, testing for correctness can not be done only through a linear number of preemptions.The majority of the related work focuses on systems of two criticality levels due to the difficulty of the problem. But for automotive and airborne systems, industrial standards define four or five criticality levels, which motivated us to propose a scheduling algorithm that schedules mixed-criticality systems with theoretically any number of criticality levels. We show experimentally that it has higher success rates compared to the state of the art.We illustrate how our scheduling algorithm, or any algorithm that generates a single time-triggered table for each criticality mode, can be used as a recovery strategy to ensure the safety of the system in case of certain failures.Finally, we propose a high level concurrency language and a model for designing an MC-system with coarse grained multi-core interference.

Mixed-criticality Real-time Task Scheduling with Graceful Degradation

"The mixed-criticality real-time systems implement functionalities of different degrees of importance (or criticalities) upon a shared platform. In traditional mixed-criticality systems, under a hi mode switch, no guaranteed service is provided to lo-criticality tasks. After a mode switch, only hi-criticality tasks are considered for execution while no guarantee is made to the lo-criticality tasks. However, with careful optimistic design, a certain degree of service guarantee can be provided to lo-criticality tasks upon a mode switch. This concept is broadly known as graceful degradation. Guaranteed graceful degradation provides a better quality of service as well as it utilizes the system resource more efficiently. In this thesis, we study two efficient techniques of graceful degradation. First, we study a mixed-criticality scheduling technique where graceful degradation is provided in the form of minimum cumulative completion rates. We present two easy-to-implement admission-control algorithms to determine which lo-criticality jobs to complete in hi mode. The scheduling is done by following deadline virtualization, and two heuristics are shown for virtual deadline settings. We further study the schedulability analysis and the backward mode switch conditions, which are proposed and proved in (Guo et al., 2018). Next, we present a probabilistic scheduling technique for mixed-criticality tasks on multiprocessor systems where a system-wide permitted failure probability is known. The schedulability conditions are derived along with the processor allocation scheme. The work is extended from (Guo et al., 2015), where the probabilistic model is first introduced for independent task scheduling on a uniprocessor platform. We further consider the failure dependency between tasks while scheduling on multiprocessor platforms. We provide related theoretical analysis to show the correctness of our work. To show the effectiveness of our proposed techniques, we conduct a detailed experimental evaluation under different circumstances"--Abstract, page iii.