Mixed-Criticality Scheduling to Minimize Makespan

In the mixed-criticality job model, each job is characterized by two execution time parameters, representing a smaller (less conservative) estimate and a larger (more conservative) estimate on its actual, unknown, execution time. Each job is further classified as being either less critical or more critical. The desired execution semantics are that all jobs should execute correctly provided all jobs complete upon being allowed to execute for up to the smaller of their execution time estimates, whereas if some jobs need to execute beyond their smaller execution time estimates (but not beyond their larger execution time estimates), then only the jobs classified as being more critical are required to execute correctly. The scheduling of collections of such mixed-criticality jobs upon identical multiprocessor platforms in order to minimize the makespan is considered here

A Measurement-Based Model for Parallel Real-Time Tasks

Under the federated paradigm of multiprocessor scheduling, a set of processors is reserved for the exclusive use of each real-time task. If tasks are characterized very conservatively (as is typical in safety-critical systems), it is likely that most invocations of the task will have computational demand far below the worst-case characterization, and could have been scheduled correctly upon far fewer processors than were assigned to it assuming the worst-case characterization of its run-time behavior. Provided we could safely determine during run-time when all the processors are going to be needed, for the rest of the time the unneeded processors could be idled in low-energy "sleep" mode, or used for executing non-real time work in the background. In this paper we propose a model for representing parallelizable real-time tasks in a manner that permits us to do so. Our model does not require us to have fine-grained knowledge of the internal structure of the code represented by the task; rather, it characterizes each task by a few parameters that are obtained by repeatedly executing the code under different conditions and measuring the run-times

Modular and Reconfigurable Platform as New Philosophy for the Development of Updatable Vehicular Electronics

[EN] A new conception in the development of Electronic Control Units (ECUs), which are also called On-Board Units (OBUs), is discussed in this paper from an ontological vision oriented to the compatibility of vehicles with future technologies in the automotive field. This work also provides a new methodology in the design of On-Board vehicle units. The proposed technique is based on the concept of modular electronic units that can change their functionality depending on the modules they are consisted of. The study was initially designed at the theoretical level, analysing the problems in the sector in the face of the coexistence between vehicles today and those that are bound to appear in the near future, and that will incorporate capabilities making them connected and even autonomous. Additionally, a fully operational prototype has been developed so as to ascertain the possibilities of the proposed solution.[ES] Se presenta una nueva concepción en el desarrollo de Unidades Electrónicas de Control (ECU), también denominadas Unidades de a Bordo (OBU), desde una visión ontológica orientada en la compatibilización de los vehículos con las futuras tecnologías emergentes en el campo de la automoción. Se comienza por un estudio teórico que analiza la problemática en el sector del transporte que va a presentar la convivencia entre los vehículos actuales y los que van a ir apareciendo en el futuro; y que vendrán influenciados por conceptos tales como los vehículos conectados o los vehículos autónomos. Este artículo también aporta una nueva metodología en el diseño de unidades vehiculares de a bordo, basada en el concepto de unidades electrónicas modulares que definen su funcionalidad en base a los módulos que le sean acoplados. Adicionalmente se ha desarrollado un prototipo completo y totalmente funcional con el fin de analizar las posibilidades de la solución propuesta.Este trabajo ha sido realizado parcialmente gracias al apoyo recibido mediante la resolución del 31/07/2014, publicada por la Universidad de Castilla-La Mancha, que establece las bases reguladoras de la convocatoria para contratos predoctorales con objeto de preparar nuevos investigadores bajo el Plan Propio de I+D+i. [2014/10340]Cañas, V.; García, A.; De Las Morenas, J.; Blanco, J. (2019). Plataforma Modular Reconfigurable como Nueva Filosofía para el Desarrollo de Electrónica Vehicular Actualizable. Revista Iberoamericana de Automática e Informática. 16(2):200-211. https://doi.org/10.4995/riai.2018.9863SWORD20021116

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

Period and Computational Elasticity for Adaptive Real-Time Systems

A wide range range of real-world applications (including multimedia players, ad-hoc communication networks, online trading, radar tracking software, and other adaptive control algorithms) need adaptive adjustment to their resource utilizations at run-time, while still maintaining real-time guarantees. The elastic task model of soft real-time systems allows for the run-time manipulation of tasks’ processor utilizations in order to maintain a system-wide quality of service or accommodate needs of other tasks by assigning each task a period within a specified range. As originally presented, only sequential tasks executing on a single processor were considered. However, in the two decades since the elastic task model was first introduced, multiprocessor systems have become increasingly prevalent. This dissertation appropriately extends the elastic task model to include both multiprocessor scheduling of sequential adaptive tasks and scheduling of adaptive tasks with internal parallelism. It also introduces novel elastic concepts in which 1) tasks can vary their computational loads rather than their periods and 2) the more realistic scenario in which tasks are allowed to adapt among a discrete set of candidate processor utilizations rather than over a continuous range. A runtime system for parallel elastic tasks is also presented and used to demonstrate the benefit of discrete elastic scheduling by enabling adaptation in the application domain of real-time hybrid simulation (RTHS)

Parallel Real-Time Scheduling for Latency-Critical Applications

In order to provide safety guarantees or quality of service guarantees, many of today\u27s systems consist of latency-critical applications, e.g. applications with timing constraints. The problem of scheduling multiple latency-critical jobs on a multiprocessor or multicore machine has been extensively studied for sequential (non-parallizable) jobs and different system models and different objectives have been considered. However, the computational requirement of a single job is still limited by the capacity of a single core. To provide increasingly complex functionalities of applications and to complete their higher computational demands within the same or even more stringent timing constraints, we must exploit the internal parallelism of jobs, where individual jobs are parallel programs and can potentially utilize more than one core in parallel. However, there is little work considering scheduling multiple parallel jobs that are latency-critical. This dissertation focuses on developing new scheduling strategies, analysis tools, and practical platform design techniques to enable efficient and scalable parallel real-time scheduling for latency-critical applications on multicore systems. In particular, the research is focused on two types of systems: (1) static real-time systems for tasks with deadlines where the temporal properties of the tasks that need to execute is known a priori and the goal is to guarantee the temporal correctness of the tasks prior to their executions; and (2) online systems for latency-critical jobs where multiple jobs arrive over time and the goal to optimize for a performance objective of jobs during the execution. For static real-time systems for parallel tasks, several scheduling strategies, including global earliest deadline first, global rate monotonic and a novel federated scheduling, are proposed, analyzed and implemented. These scheduling strategies have the best known theoretical performance for parallel real-time tasks under any global strategy, any fixed priority scheduling and any scheduling strategy, respectively. In addition, federated scheduling is generalized to systems with multiple criticality levels and systems with stochastic tasks. Both numerical and empirical experiments show that federated scheduling and its variations have good schedulability performance and are efficient in practice. For online systems with multiple latency-critical jobs, different online scheduling strategies are proposed and analyzed for different objectives, including maximizing the number of jobs meeting a target latency, maximizing the profit of jobs, minimizing the maximum latency and minimizing the average latency. For example, a simple First-In-First-Out scheduler is proven to be scalable for minimizing the maximum latency. Based on this theoretical intuition, a more practical work-stealing scheduler is developed, analyzed and implemented. Empirical evaluations indicate that, on both real world and synthetic workloads, this work-stealing implementation performs almost as well as an optimal scheduler

Energy-Aware Real-Time Scheduling on Heterogeneous and Homogeneous Platforms in the Era of Parallel Computing

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform\u27s computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time embedded system application\u27s internal parallelism to reduce the worst-case energy consumption (WCEC), an essential requirement for energy-constrained systems. First, we propose an energy-sub-optimal scheduler, assuming the per-core speed tuning feature for each processor. Then we extend our solution to adapt the clustered multi-core platform, where at any given time, all the processors in the same cluster run at the same speed. We also present an analysis to exploit a task\u27s probabilistic information to improve the average-case energy consumption (ACEC), a common non-functional requirement of embedded systems. Due to the strict requirement of temporal correctness, the majority of the real-time system analysis considered the worst-case scenario, leading to resource over-provisioning and cost. The mixed-criticality (MC) framework was proposed to minimize energy consumption and resource over-provisioning. MC scheduling has received considerable attention from the real-time system research community, as it is crucial to designing safety-critical real-time systems. This thesis further addresses energy-aware scheduling of real-time tasks in an MC platform, where tasks with varying criticality levels (i.e., importance) are integrated into a common platform. We propose an algorithm GEDF-VD for scheduling MC tasks with internal parallelism in a multiprocessor platform. We also prove the correctness of GEDF-VD, provide a detailed quantitative evaluation, and reported extensive experimental results. Finally, we present an analysis to exploit a task\u27s probabilistic information at their respective criticality levels. Our proposed approach reduces the average-case energy consumption while satisfying the worst-case timing requirement