Scheduling policies and system software architectures for mixed-criticality computing

Mixed-criticality model of computation is being increasingly adopted in timing-sensitive systems. The model not only ensures that the most critical tasks in a system never fails, but also aims for better systems resource utilization in normal condition. In this report, we describe the widely used mixed-criticality task model and fixed-priority scheduling algorithms for the model in uniprocessors. Because of the necessity by the mixed-criticality task model and scheduling policies, isolation, both temporal and spatial, among tasks is one of the main requirements from the system design point of view. Different virtualization techniques have been used to design system software architecture with the goal of isolation. We discuss such a few system software architectures which are being and can be used for mixed-criticality model of computation

Schedulability analysis and optimization of time-partitioned distributed real-time systems

RESUMEN: La creciente complejidad de los sistemas de control modernos lleva a muchas empresas a tener que re-dimensionar o re-diseñar sus soluciones para adecuarlas a nuevas funcionalidades y requisitos. Un caso paradigmático de esta situación se ha dado en el sector ferroviario, donde la implementación de las aplicaciones de señalización se ha llevado a cabo empleando técnicas tradicionales que, si bien ahora mismo cumplen con los requisitos básicos, su rendimiento temporal y escalabilidad funcional son sustancialmente mejorables. A partir de las soluciones propuestas en esta tesis, además de contribuir a la validación de sistemas que requieren certificación de seguridad funcional, también se creará la tecnología base de análisis de planificabilidad y optimización de sistemas de tiempo real distribuidos generales y también basados en particionado temporal, que podrá ser aplicada en distintos entornos en los que los sistemas ciberfísicos juegan un rol clave, por ejemplo en aplicaciones de Industria 4.0, en los que pueden presentarse problemas similares en el futuro.ABSTRACT:he increasing complexity of modern control systems leads many companies to have to resize or redesign their solutions to adapt them to new functionalities and requirements. A paradigmatic case of this situation has occurred in the railway sector, where the implementation of signaling applications has been carried out using traditional techniques that, although they currently meet the basic requirements, their time performance and functional scalability can be substantially improved. From the solutions proposed in this thesis, besides contributing to the assessment of systems that require functional safety certification, the base technology for schedulability analysis and optimization of general as well as time-partitioned distributed real-time systems will be derived, which can be applied in different environments where cyber-physical systems play a key role, for example in Industry 4.0 applications, where similar problems may arise in the future

k2U: A General Framework from k-Point Effective Schedulability Analysis to Utilization-Based Tests

To deal with a large variety of workloads in different application domains in real-time embedded systems, a number of expressive task models have been developed. For each individual task model, researchers tend to develop different types of techniques for deriving schedulability tests with different computation complexity and performance. In this paper, we present a general schedulability analysis framework, namely the k2U framework, that can be potentially applied to analyze a large set of real-time task models under any fixed-priority scheduling algorithm, on both uniprocessor and multiprocessor scheduling. The key to k2U is a k-point effective schedulability test, which can be viewed as a "blackbox" interface. For any task model, if a corresponding k-point effective schedulability test can be constructed, then a sufficient utilization-based test can be automatically derived. We show the generality of k2U by applying it to different task models, which results in new and improved tests compared to the state-of-the-art. Analogously, a similar concept by testing only k points with a different formulation has been studied by us in another framework, called k2Q, which provides quadratic bounds or utilization bounds based on a different formulation of schedulability test. With the quadratic and hyperbolic forms, k2Q and k2U frameworks can be used to provide many quantitive features to be measured, like the total utilization bounds, speed-up factors, etc., not only for uniprocessor scheduling but also for multiprocessor scheduling. These frameworks can be viewed as a "blackbox" interface for schedulability tests and response-time analysis

Resource-Efficient Scheduling Of Multiprocessor Mixed-Criticality Real-Time Systems

Timing guarantee is critical to ensure the correctness of embedded software systems that interact with the physical environment. As modern embedded real-time systems evolves, they face three challenges: resource constraints, mixed-criticality, and multiprocessors. This dissertation focuses on resource-efficient scheduling techniques for mixed-criticality systems on multiprocessor platforms. While Mixed-Criticality (MC) scheduling has been extensively studied on uniprocessor plat- forms, the problem on multiprocessor platforms has been largely open. Multiprocessor al- gorithms are broadly classified into two categories: global and partitioned. Global schedul- ing approaches use a global run-queue and migrate tasks among processors for improved schedulability. Partitioned scheduling approaches use per processor run-queues and can reduce preemption/migration overheads in real implementation. Existing global scheduling schemes for MC systems have suffered from low schedulability. Our goal in the first work is to improve the schedulability of MC scheduling algorithms. Inspired by the fluid scheduling model in a regular (non-MC) domain, we have developed the MC-Fluid scheduling algo- rithm that executes a task with criticality-dependent rates. We have evaluated MC-Fluid in terms of the processor speedup factor: MC-Fluid is a multiprocessor MC scheduling algo- rithm with a speed factor of 4/3, which is known to be optimal. In other words, MC-Fluid can schedule any feasible mixed-criticality task system if each processor is sped up by a factor of 4/3. Although MC-Fluid is speedup-optimal, it is not directly implementable on multiprocessor platforms of real processors due to the fractional processor assumption where multiple task can be executed on one processor at the same time. In the second work, we have considered the characteristic of a real processor (executing only one task at a time) and have developed the MC-Discrete scheduling algorithm for regular (non-fluid) scheduling platforms. We have shown that MC-Discrete is also speedup-optimal. While our previous two works consider global scheduling approaches, our last work con- siders partitioned scheduling approaches, which are widely used in practice because of low implementation overheads. In addition to partitioned scheduling, the work consid- ers the limitation of conventional MC scheduling algorithms that drops all low-criticality tasks when violating a certain threshold of actual execution times. In practice, the system designer wants to execute the tasks as much as possible. To address the issue, we have de- veloped the MC-ADAPT scheduling framework under uniprocessor platforms to drop as few low-criticality tasks as possible. Extending the framework with partitioned multiprocessor platforms, we further reduce the dropping of low-criticality tasks by allowing migration of low-criticality tasks at the moment of a criticality switch. We have evaluated the quality of task dropping solution in terms of speedup factor. In existing work, the speedup factor has been used to evaluate MC scheduling algorithms in terms of schedulability under the worst-case scheduling scenario. In this work, we apply the speedup factor to evaluate MC scheduling algorithms in terms of the quality of their task dropping solution under various MC scheduling scenarios. We have derived that MC-ADAPT has a speedup factor of 1.618 for task dropping solution