Response Time Analysis for Thermal-Aware Real-Time Systems Under Fixed-Priority Scheduling

International audienceThis paper investigates schedulability analysis for thermal-aware real-time systems. Thermal constraints are becoming more and more critical in new generation miniaturized embedded systems, e.g. Medicals implants. As part of this work, we adapt the PFPasap algorithm proposed in [1] for energy-harvesting systems to thermal-aware ones. We prove its optimality for non-concrete1 fixed-priority task sets and propose a response-time analysis based on worst-case response-time upper bounds. We evaluate the efficacy of the proposed bounds via extensive simulation over randomly-generated task systems

Using Imprecise Computing for Improved Real-Time Scheduling

Conventional hard real-time scheduling is often overly pessimistic due to the worst case execution time estimation. The pessimism can be mitigated by exploiting imprecise computing in applications where occasional small errors are acceptable. This leverage is investigated in a few previous works, which are restricted to preemptive cases. We study how to make use of imprecise computing in uniprocessor non-preemptive real-time scheduling, which is known to be more difficult than its preemptive counterpart. Several heuristic algorithms are developed for periodic tasks with independent or cumulative errors due to imprecision. Simulation results show that the proposed techniques can significantly improve task schedulability and achieve desired accuracy– schedulability tradeoff. The benefit of considering imprecise computing is further confirmed by a prototyping implementation in Linux system. Mixed-criticality system is a popular model for reducing pessimism in real-time scheduling while providing guarantee for critical tasks in presence of unexpected overrun. However, it is controversial due to some drawbacks. First, all low-criticality tasks are dropped in high-criticality mode, although they are still needed. Second, a single high-criticality job overrun leads to the pessimistic high-criticality mode for all high-criticality tasks and consequently resource utilization becomes inefficient. We attempt to tackle aforementioned two limitations of mixed-criticality system simultaneously in multiprocessor scheduling, while those two issues are mostly focused on uniprocessor scheduling in several recent works. We study how to achieve graceful degradation of low-criticality tasks by continuing their executions with imprecise computing or even precise computing if there is sufficient utilization slack. Schedulability conditions under this Variable-Precision Mixed-Criticality (VPMC) system model are investigated for partitioned scheduling and global fpEDF-VD scheduling. And a deferred switching protocol is introduced so that the chance of switching to high-criticality mode is significantly reduced. Moreover, we develop a precision optimization approach that maximizes precise computing of low-criticality tasks through 0-1 knapsack formulation. Experiments are performed through both software simulations and Linux proto- typing with consideration of overhead. Schedulability of the proposed methods is studied so that the Quality-of-Service for low-criticality tasks is improved with guarantee of satisfying all deadline constraints. The proposed precision optimization can largely reduce computing errors compared to constantly executing low-criticality tasks with imprecise computing in high-criticality mode

Control techniques for thermal-aware energy-efficient real time multiprocessor scheduling

La utilización de microprocesadores multinúcleo no sólo es atractiva para la industria sino que en muchos ámbitos es la única opción. La planificación tiempo real sobre estas plataformas es mucho más compleja que sobre monoprocesadores y en general empeoran el problema de sobre-diseño, llevando a la utilización de muchos más procesadores /núcleos de los necesarios. Se han propuesto algoritmos basados en planificación fluida que optimizan la utilización de los procesadores, pero hasta el momento presentan en general inconvenientes que los alejan de su aplicación práctica, no siendo el menor el elevado número de cambios de contexto y migraciones.Esta tesis parte de la hipótesis de que es posible diseñar algoritmos basados en planificación fluida, que optimizan la utilización de los procesadores, cumpliendo restricciones temporales, térmicas y energéticas, con un bajo número de cambios de contexto y migraciones, y compatibles tanto con la generación fuera de línea de ejecutivos cíclicos atractivos para la industria, como de planificadores que integran técnicas de control en tiempo de ejecución que permiten la gestión eficiente tanto de tareas aperiódicas como de desviaciones paramétricas o pequeñas perturbaciones.A este respecto, esta tesis contribuye con varias soluciones. En primer lugar, mejora una metodología de modelo que representa todas las dimensiones del problema bajo un único formalismo (Redes de Petri Continuas Temporizadas). En segundo lugar, propone un método de generación de un ejecutivo cíclico, calculado en ciclos de procesador, para un conjunto de tareas tiempo real duro sobre multiprocesadores que optimiza la utilización de los núcleos de procesamiento respetando también restricciones térmicas y de energía, sobre la base de una planificación fluida. Considerar la sobrecarga derivada del número de cambios de contexto y migraciones en un ejecutivo cíclico plantea un dilema de causalidad: el número de cambios de contexto (y en consecuencia su sobrecarga) no se conoce hasta generar el ejecutivo cíclico, pero dicho número no se puede minimizar hasta que se ha calculado. La tesis propone una solución a este dilema mediante un método iterativo de convergencia demostrada que logra minimizar la sobrecarga mencionada.En definitiva, la tesis consigue explotar la idea de planificación fluida para maximizar la utilización (donde maximizar la utilización es un gran problema en la industria) generando un sencillo ejecutivo cíclico de mínima sobrecarga (ya que la sobrecarga implica un gran problema de los planificadores basados en planificación fluida).Finalmente, se propone un método para utilizar las referencias de la planificación fuera de línea establecida en el ejecutivo cíclico para su seguimiento por parte de un controlador de frecuencia en línea, de modo que se pueden afrontar pequeñas perturbaciones y variaciones paramétricas, integrando la gestión de tareas aperiódicas (tiempo real blando) mientras se asegura la integridad de la ejecución del conjunto de tiempo real duro.Estas aportaciones constituyen una novedad en el campo, refrendada por las publicaciones derivadas de este trabajo de tesis.<br /

Energy-efficient thermal-aware multiprocessor scheduling for real-time tasks using TCPNs

We present an energy-effcient thermal-aware real-time global scheduler for a set of hard real-time (HRT) tasks running on a multiprocessor system. This global scheduler fulfills the thermal and temporal constraints by handling two independent variables, the task allocation time and the selection of clock frequency. To achieve its goal, the proposed scheduler is split into two stages. An off-line stage, based on a deadline partitioning scheme, computes the cycles that the HRT tasks must run per deadline interval at the minimum clock frequency to save energy while honoring the temporal and thermal constraints, and computes the maximum frequency at which the system can run below the maximum temperature. Then, an on-line, event-driven stage performs global task allocation applying a Fixed-Priority Zero-Laxity policy, reducing the overhead of quantum-based or interval-based global schedulers. The on-line stage embodies an adaptive scheduler that accepts or rejects soft RT aperiodic tasks throttling CPU frequency to the upper lowest available one to minimize power consumption while meeting time and thermal constraints. This approach leverages the best of two worlds: the off-line stage computes an ideal discrete HRT multiprocessor schedule, while the on-line stage manage soft real-time aperiodic tasks with minimum power consumption and maximum CPU utilization

Green Scheduling of Control Systems

Electricity usage under peak load conditions can cause issues such as reduced power quality and power outages. For this reason, commercial electricity customers are often subject to demand-based pricing, which charges very high prices for peak electricity demand. Consequently, reducing peaks in electricity demand is desirable for both economic and reliability reasons. In this thesis, we investigate the peak demand reduction problem from the perspective of safe scheduling of control systems under resource constraint. To this end, we propose Green Scheduling as an approach to schedule multiple interacting control systems within a constrained peak demand envelope while ensuring that safety and operational conditions are facilitated. The peak demand envelope is formulated as a constraint on the number of binary control inputs that can be activated simultaneously. Using two different approaches, we establish a range of sufficient and necessary schedulability conditions for various classes of affine dynamical systems. The schedulability analysis methods are shown to be scalable for large-scale systems consisting of up to 1000 subsystems. We then develop several scheduling algorithms for the Green Scheduling problem. First, we develop a periodic scheduling synthesis method, which is simple and scalable in computation but does not take into account the influence of disturbances. We then improve the method to be robust to small disturbances while preserving the simplicity and scalability of periodic scheduling. However the improved algorithm usually result in fast switching of the control inputs. Therefore, event-triggered and self-triggered techniques are used to alleviate this issue. Next, using a feedback control approach based on attracting sets and robust control Lyapunov functions, we develop event-triggered and self-triggered scheduling algorithms that can handle large disturbances affecting the system. These algorithms can also exploit prediction of the disturbances to improve their performance. Finally, a scheduling method for discrete-time systems is developed based on backward reachability analysis. The effectiveness of the proposed approach is demonstrated by an application to scheduling of radiant heating and cooling systems in buildings. Green Scheduling is able to significantly reduce the peak electricity demand and the total electricity consumption of the radiant systems, while maintaining thermal comfort for occupants

Thermal-aware real-time scheduling using timed continuous Petri Nets

We present a thermal-aware, hard real-time (HRT) global scheduler for a multiprocessor system designed upon three novel techinques. First, we present a modeling methodology based on Timed Continuous Petri nets (TCPN) that yields a complete state variable model, including job arrivals, CPU usage, power, and thermal behavior. The model is accurate and avoids the calibration stage of RC thermal models. Second, based on this model, a linear programming problem (LPP) determines the existence of a feasible HRT thermal-aware schedule. Last, a sliding-mode controller and an online discretization algorithm implement the global HRT scheduler, which is capable of managing thermal constraints, context switching, migrations, and disturbances

Software Fault Tolerance in Real-Time Systems: Identifying the Future Research Questions

Tolerating hardware faults in modern architectures is becoming a prominent problem due to the miniaturization of the hardware components, their increasing complexity, and the necessity to reduce the costs. Software-Implemented Hardware Fault Tolerance approaches have been developed to improve the system dependability to hardware faults without resorting to custom hardware solutions. However, these come at the expense of making the satisfaction of the timing constraints of the applications/activities harder from a scheduling standpoint. This paper surveys the current state of the art of fault tolerance approaches when used in the context real-time systems, identifying the main challenges and the cross-links between these two topics. We propose a joint scheduling-failure analysis model that highlights the formal interactions among software fault tolerance mechanisms and timing properties. This model allows us to present and discuss many open research questions with the final aim to spur the future research activities

Energy-Efficient Thermal-Aware Scheduling for RT Tasks Using TCPN

This work leverages TCPNs to design an energy-efficient, thermal-aware real-time scheduler for a multiprocessor system that normally runs in a low state energy at maximum system utilization but its capable of increasing the clock frequency to serve aperiodic tasks, optimizing energy, and honoring temporal and thermal constraints. An off-line stage computes the minimum frequency required to run the periodic tasks at maximum CPU utilization, the proportion of each task''s job to be run on each CPU, the maximum clock frequency that keeps temperature under a limit, and the available cycles (slack) with respect to the system with minimum frequency. Then, a Zero-Laxity online scheduler dispatches the periodic tasks according to the offline calculation. Upon the arrival of aperiodic tasks, it increases clock frequency in such a way that all periodic and aperiodic tasks are properly executed. Thermal and temporal requirements are always guaranteed, and energy consumption is minimized

Mixed Criticality Systems - A Review : (13th Edition, February 2022)

This review covers research on the topic of mixed criticality systems that has been published since Vestal’s 2007 paper. It covers the period up to end of 2021. The review is organised into the following topics: introduction and motivation, models, single processor analysis (including job-based, hard and soft tasks, fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, related topics, realistic models, formal treatments, systems issues, industrial practice and research beyond mixed-criticality. A list of PhDs awarded for research relating to mixed-criticality systems is also included

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