Ordonnancement des systèmes avec différents niveaux de criticité

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.Les systèmes temps-réel critiques doivent exécuter leurs tâches dans les délais impartis. En cas de défaillance, des événements peuvent avoir des catastrophes économiques. Des classifications des défaillances par rapport aux niveaux des risques encourus ont été établies, en particulier dans les domaines des transports aéronautique et automobile. Des niveaux de criticité sont attribués aux différentes fonctions des systèmes suivant les risques encourus lors d'une défaillance et des probabilités d'apparition de celles-ci. Ces différents niveaux de criticité influencent les choix d'architecture logicielle et matérielle ainsi que le type de composants utilisés pour sa réalisation. Les systèmes temps-réels modernes ont tendance à intégrer sur une même plateforme de calcul plusieurs applications avec différents niveaux de criticité. Cette intégration est nécessaire pour des systèmes modernes comme par exemple les drones (UAV) afin de réduire le coût, le poids et la consommation d'énergie. Malheureusement, elle conduit à des difficultés importantes lors de leurs conceptions. En plus, ces systèmes doivent être certifiés en prenant en compte ces différents niveaux de criticités.Il est bien connu que le problème d'ordonnancement des systèmes avec différents niveaux de criticités représente un des plus grand défi dans le domaine de systèmes temps-réel. Les techniques traditionnelles proposent comme solution l’isolation complète entre les niveaux de criticité ou bien une certification globale au plus haut niveau. Malheureusement, une telle solution conduit à une mauvaise des ressources et à la perte de l’avantage de cette intégration. En 2007, Vestal a proposé un modèle pour représenter les systèmes avec différents niveaux de criticité dont les tâches ont plusieurs temps d’exécution, un pour chaque niveau de criticité. En outre, les conditions de validité des stratégies d’ordonnancement ont été définies de manière formelle, permettant ainsi aux tâches les moins critiques d’échapper aux délais, voire d’être abandonnées en cas de défaillance ou de situation d’urgence.Les politiques de planification conventionnelles et les tests d’ordonnoncement se sont révélés inadéquats.Dans cette thèse, nous contribuons à l’étude de l’ordonnancement dans les systèmes avec différents niveaux de criticité. La surcharge d'un système est représentée sous la forme d'un ensemble de tâches pouvant décrire l'exécution sur l'hyper-période de tâches ou sur une durée donnée. Ce modèle nous permet d’étudier la viabilité des tests de correction basés sur la simulation pour les systèmes avec différents niveaux de criticité. Nous montrons que les tests de simulation peuvent toujours être utilisés pour ces systèmes, et la possibilité de l’ordonnancement du pire des scénarios ne suffit plus, même pour le cas de l’ordonnancement avec priorité fixe. Nous montrons que les politiques d'ordonnancement ne sont généralement pas prévisibles. Nous définissons le concept de faible prévisibilité pour les systèmes avec différents niveaux de criticité et nous montrons ensuite qu'une classe spécifique de stratégies à priorité fixe sont faiblement prévisibles. Nous proposons deux tests de correction basés sur la simulation qui fonctionnent pour des stratégies faiblement prévisibles.Nous montrons également que, contrairement à ce que l’on croyait, le contrôle de l’exactitude ne peut se faire que par l’intermédiaire d’un nombre linéaire de préemptions.La majorité des travaux reliés à notre domaine portent sur des systèmes à deux niveaux de criticité en raison de la difficulté du problème. Mais pour les systèmes automobiles et aériens, les normes industrielles définissent quatre ou cinq niveaux de criticité, ce qui nous a motivés à proposer un algorithme de planification qui planifie les systèmes à criticité mixte avec théoriquement un nombre quelconque de niveaux de criticité. Nous montrons expérimentalement que le taux de réussite est supérieur à celui de l’état de la technique

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

An extensible framework for multicore response time analysis

In this paper, we introduce a multicore response time analysis (MRTA) framework, which decouples response time analysis from a reliance on context independent WCET values. Instead, the analysis formulates response times directly from the demands placed on different hardware resources. The MRTA framework is extensible to different multicore architectures, with a variety of arbitration policies for the common interconnects, and different types and arrangements of local memory. We instantiate the framework for single level local data and instruction memories (cache or scratchpads), for a variety of memory bus arbitration policies, including: Round-Robin, FIFO, Fixed-Priority, Processor-Priority, and TDMA, and account for DRAM refreshes. The MRTA framework provides a general approach to timing verification for multicore systems that is parametric in the hardware configuration and so can be used at the architectural design stage to compare the guaranteed levels of real-time performance that can be obtained with different hardware configurations. We use the framework in this way to evaluate the performance of multicore systems with a variety of different architectural components and policies. These results are then used to compose a predictable architecture, which is compared against a reference architecture designed for good average-case behaviour. This comparison shows that the predictable architecture has substantially better guaranteed real-time performance, with the precision of the analysis verified using cycle-accurate simulation

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Analyses and optimizations of timing-constrained embedded systems considering resource synchronization and machine learning approaches

Nowadays, embedded systems have become ubiquitous, powering a vast array of applications from consumer electronics to industrial automation. Concurrently, statistical and machine learning algorithms are being increasingly adopted across various application domains, such as medical diagnosis, autonomous driving, and environmental analysis, offering sophisticated data analysis and decision-making capabilities. As the demand for intelligent and time-sensitive applications continues to surge, accompanied by growing concerns regarding data privacy, the deployment of machine learning models on embedded devices has emerged as an indispensable requirement. However, this integration introduces both significant opportunities for performance enhancement and complex challenges in deployment optimization. On the one hand, deploying machine learning models on embedded systems with limited computational capacity, power budgets, and stringent timing requirements necessitates additional adjustments to ensure optimal performance and meet the imposed timing constraints. On the other hand, the inherent capabilities of machine learning, such as self-adaptation during runtime, prove invaluable in addressing challenges encountered in embedded systems, aiding in optimization and decision-making processes. This dissertation introduces two primary modifications for the analyses and optimizations of timing-constrained embedded systems. For one thing, it addresses the relatively long access times required for shared resources of machine learning tasks. For another, it considers the limited communication resources and data privacy concerns in distributed embedded systems when deploying machine learning models. Additionally, this work provides a use case that employs a machine learning method to tackle challenges specific to embedded systems. By addressing these key aspects, this dissertation contributes to the analysis and optimization of timing-constrained embedded systems, considering resource synchronization and machine learning models to enable improved performance and efficiency in real-time applications with stringent constraints