Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds

Heterogeneous computing platforms with multiple types of computing resources have been widely used in many industrial systems to process dataflow tasks with pre-defined affinity of tasks to subgroups of resources. For many dataflow workloads with soft real-time requirements, guaranteeing fast and bounded response times is often the objective. This paper presents a new set of analysis techniques showing that a classical real-time scheduler, namely earliest-deadline first (EDF), is able to support dataflow tasks scheduled on such heterogeneous platforms with provably bounded response times while incurring no resource capacity loss, thus proving EDF to be an optimal solution for this scheduling problem. Experiments using synthetic workloads with widely varied parameters also demonstrate that the magnitude of the response time bounds yielded under the proposed analysis is reasonably small under all scenarios. Compared to the state-of-the-art soft real-time analysis techniques, our test yields a 68% reduction on response time bounds on average. This work demonstrates the potential of applying EDF into practical industrial systems containing dataflow-based workloads that desire guaranteed bounded response times

Multiprocessor Scheduling meets the Industrial Wireless

This survey covers schedulability analysis approaches that have been recently proposed for multi-hop and multi-channel wireless sensor and actuator networks in the industrial control process domain. It reviews results with a focus on WirelessHART-like networks. The paper address the mapping of multi-channel transmission scheduling to multiprocessor scheduling theory, and recognize it as the key aspect of the research direction covered by this survey. It also provides a taxonomy of the existing approaches concerning this direction, and discuss its main features and evolution. The survey identifies open issues, key research challenges, and future directions

Compensating Adaptive Mixed Criticality Scheduling

The majority of prior academic research into mixed criticality systems assumes that if high-criticality tasks continue to execute beyond the execution time limits at which they would normally finish, then further workload due to low-criticality tasks may be dropped in order to ensure that the high-criticality tasks can still meet their deadlines. Industry, however, takes a different view of the importance of low-criticality tasks, with many practical systems unable to tolerate the abandonment of such tasks. In this paper, we address the challenge of supporting genuinely graceful degradation in mixed criticality systems, thus avoiding the abandonment problem. We explore the Compensating Adaptive Mixed Criticality (C-AMC) scheduling scheme. C-AMC ensures that both high- and low-criticality tasks meet their deadlines in both normal and degraded modes. Under C-AMC, jobs of low-criticality tasks, released in degraded mode, execute imprecise versions that provide essential functionality and outputs of sufficient quality, while also reducing the overall workload. This compensates, at least in part, for the overload due to the abnormal behavior of high-criticality tasks. C-AMC is based on fixed-priority preemptive scheduling and hence provides a viable migration path along which industry can make an evolutionary transition from current practice

Mixed Criticality on Multi-cores Accounting for Resource Stress and Resource Sensitivity

The most significant trend in real-time systems design in recent years has been the adoption of multi-core processors and the accompanying integration of functionality with different criticality levels onto the same hardware platform. This paper integrates mixed criticality aspects and assurances within a multi-core system model. It bounds cross-core contention and interference by considering the impact on task execution times due to the stress on shared hardware resources caused by co-runners, and each task’s sensitivity to that resource stress. Schedulability analysis is derived for four mixed criticality scheduling schemes based on partitioned fixed priority preemptive scheduling. Each scheme provides robust timing guarantees for high criticality tasks, ensuring that their timing constraints cannot be jeopardized by the behavior or misbehavior of low criticality tasks

Graceful Degradation in Semi-Clairvoyant Scheduling

In the Vestal model of mixed-criticality systems, jobs are characterized by multiple different estimates of their actual, but unknown, worst-case execution time (WCET) parameters. Some recent research has focused upon a semi-clairvoyant model for mixed-criticality systems in which it is assumed that each job reveals upon arrival which of its WCET parameters it will respect. We study the problem of scheduling such semi-clairvoyant systems to ensure graceful degradation of service to less critical jobs in the event that the systems exhibit high-criticality behavior. We propose multiple different interpretations of graceful degradation in such systems, and derive efficient scheduling algorithms that are capable of ensuring graceful degradation under these different interpretations

Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing

Recent work showed that semi-partitioned scheduling can achieve near-optimal schedulability performance, is simpler to implement compared to global scheduling, and less heavier in terms of runtime overhead, thus resulting in an excellent choice for implementing real-world systems. However, semi-partitioned scheduling typically leverages an off-line design to allocate tasks across the available processors, which requires a-priori knowledge of the workload. Conversely, several simple global schedulers, as global earliest-deadline first (G-EDF), can transparently support dynamic workload without requiring a task-allocation phase. Nonetheless, such schedulers exhibit poor worst-case performance. This work proposes a semi-partitioned approach to efficiently schedule dynamic real-time workload on a multiprocessor system. A linear-time approximation for the C=D splitting scheme under partitioned EDF scheduling is first presented to reduce the complexity of online scheduling decisions. Then, a load-balancing algorithm is proposed for admitting new real-time workload in the system with limited workload re-allocation. A large-scale experimental study shows that the linear-time approximation has a very limited utilization loss compared to the exact technique and the proposed approach achieves very high schedulability performance, with a consistent improvement on G-EDF and pure partitioned EDF scheduling

Efficiently Approximating the Probability of Deadline Misses in Real-Time Systems

This paper explores the probability of deadline misses for a set of constrained-deadline sporadic soft real-time tasks on uniprocessor platforms. We explore two directions to evaluate the probability whether a job of the task under analysis can finish its execution at (or before) a testing time point t. One approach is based on analytical upper bounds that can be efficiently computed in polynomial time at the price of precision loss for each testing point, derived from the well-known Hoeffding\u27s inequality and the well-known Bernstein\u27s inequality. Another approach convolutes the probability efficiently over multinomial distributions, exploiting a series of state space reduction techniques, i.e., pruning without any loss of precision, and approximations via unifying equivalent classes with a bounded loss of precision. We demonstrate the effectiveness of our approaches in a series of evaluations. Distinct from the convolution-based methods in the literature, which suffer from the high computation demand and are applicable only to task sets with a few tasks, our approaches can scale reasonably without losing much precision in terms of the derived probability of deadline misses

Tracking coherence-related contention delays in real-time multicore systems

The prevailing use of multicores in Embedded Critical Systems (ECS) is multi-application workloads in which independent applications run in different cores with data sharing restricted to the communication between applications and the real-time operating system. However, thread-level parallelism is increasingly used, e.g., OpenMP, in ECS to improve individual applications' performance. At the hardware level, we are witnessing increased research efforts to master and improve multicore cache coherence that plays a key role enabling efficient data sharing among threads. Despite these efforts, the limited information provided by performance monitoring counters on cache coherence limits the understanding of coherence's impact on tasks execution time and hence, poses severe constraints to estimate tight worst-case execution time bounds. In this line, this work contributes with an analysis of the impact that cache coherence can have on application timing behavior, and a new set of low-overhead performance monitoring counters that can be used to track the coherence-related contention that different threads can cause on each other when sharing data. Our results show that the proposed performance monitoring counters effectively capture all coherence-related contention that tasks can suffer and hence are key for parallel software timing validation and verification in ECS. Furthermore, they help application optimization by providing key information about data sharing among the application threads.The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773). This work has also been partially supported by Grant PID2019-107255GB-C21 funded by MCIN/AEI/ 10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Control de la ejecución en sistemas de criticidad mixta

[Resumen] En sistemas de tiempo real y, en general, en sistemas críticos, hay una tendencia en alza en utilizar aplicaciones con diferentes niveles de criticidad. Las soluciones basadas en hipervisores son una forma de implementar sistemas de criticidad mixta ya que proporcionan aislamiento temporal y espacial. Sin embargo, la ejecución de una partición puede ser afectada por la ejecución en otros núcleos, lo que se conoce comúnmente como interferencias, poniendo en peligro la ejecución en el tiempo especificado. En este artículo se intenta contribuir dando soluciones realistas a este problema. Se propone una solución de control de la planificación con dos controladores al nivel del hipervisor. Uno de los controladores está orientado a limitar el uso de recursos compartidos a través de la limitación en el uso de los buses en los núcleos no críticos. El otro controlador mide la actividad del núcleo crítico y tomas decisiones sobre la ejecución de los núcleos no críticos.Ministerio de Economía y Competitividad; TIN2014-56158-C4-01/02Generalitat Valenciana; PROMETEOII/2014/03

Scheduling algorithms and timing analysis for hard real-time systems

Real-time systems are designed for applications in which response time is critical. As timing is a major property of such systems, proving timing correctness is of utter importance. To achieve this, a two-fold approach of timing analysis is traditionally involved: (i) worst-case execution time (WCET) analysis, which computes an upper bound on the execution time of a single job of a task running in isolation; and (ii) schedulability analysis using the WCET as the input, which determines whether multiple tasks are guaranteed to meet their deadlines. Formal models used for representing recurrent real-time tasks have traditionally been characterized by a collection of independent jobs that are released periodically. However, such a modeling may result in resource under-utilization in systems whose behaviors are not entirely periodic or independent. Examples are (i) multicore platforms where tasks share a communication fabric, like bus, for accesses to a shared memory beside processors; (ii) tasks with synchronization, where no two concurrent access to one shared resource are allowed to be in their critical section at the same time; and (iii) automotive systems, where tasks are linked to rotation (e.g., of the crankshaft, gears, or wheels). There, their activation rate is proportional to the angular velocity of a specific device. This dissertation presents multiple approaches towards designing scheduling algorithms and schedulability analysis for a variety of real-time systems with different characteristics. Specifically, we look at those design problems from the perspective of speedup factor — a metric that quantifies both the pessimism of the analysis and the non-optimality of the scheduling algorithm. The proposed solutions are shown promising by means of not only speedup factor but also extensive evaluations