On the schedulability analysis for distributed hard real-time systems

Gutiérrez, José Carlos Palencia; Gutiérrez, J. Javier; Harbour, Michael González

doi:10.1109/emwrts.1997.613774

Cited by 45 publications

(4 citation statements)

References 8 publications

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“…In a distributed system, the transmission times of the messages through the communications network must also be taken into the analysis. If we use a real-time network that is based on fixed priority messages like the CAN bus or point to point lines [5], we can model the network as if it was another processor, accounting the non-preemptability of the message packets as additional blocking time [6]. Thus, for simplicity, we will only talk about tasks and processors, although one or more of these processors may in fact be modeling a communications network.…”

Section: Analysis Of Multiprocessor and Distributed Systemsmentioning

confidence: 99%

“…However, there are two related areas in which current RMA cannot provide exact solutions to the response times: distributed hard real-time systems, and systems in which tasks suspend themselves. Current techniques for these systems are based on the assumption that all tasks are independent, and thus they lead to pessimistic results [10] [6]. If an exact or a less pessimistic technique could be accomplished, this would enable more efficient use of the computing power available in these real-time systems.…”

Section: Introductionmentioning

confidence: 99%

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Schedulability analysis for tasks with static and dynamic offsets

Palencia

Harbour

Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279)

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277

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In this paper we present an extension to current schedulability analysis techniques for periodic tasks with offsets, scheduled under a preemptive fixed priority scheduler. Previous techniques allowed only static offsets restricted to being smaller than the task periods. With the extension presented in this paper, we eliminate this restriction and we allow both static and dynamic offsets. The most significant application of this extension is in the analysis of multiprocessor and distributed systems. We show that we can achieve a significant increase of the maximum schedulable utilization by using the new technique, as opposed to using previously known worst-case analysis techniques for distributed systems. ___________________

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Section: Analysis Of Multiprocessor and Distributed Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Schedulability analysis for tasks with static and dynamic offsets

Palencia

Harbour

Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279)

Self Cite

267

277

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show abstract

“…For each group, the worst-case response time (WCRT) analysis is conducted and the schedulability is checked by comparing the deadline constraint and the estimated WCRT. We use a compositional performance analysis (CPA) to estimate the WCRT for each group ( [29], [30]). In the CPA, the WCRT analysis is performed for each PE separately and the dependency between tasks mapped to different PEs is modeled as an event stream that is specified by a tuple (period, jitter, the minimum distance between two events).…”

Section: ) Worst-case Response Time Analysismentioning

confidence: 99%

A Novel Technique to Support Deep Learning Applications in a Model-Based Embedded Software Design Methodology

Kim

Son

2023

IEEE Access

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As deep learning applications are getting popular in embedded systems, how to support deep learning applications in the model-based embedded software design methodology becomes a challenging problem. A previous solution is to represent each deep learning application with a model. However, it requires significant efforts to translate specifications and obtain good performance by applying optimization techniques to deep learning applications. In this work, we propose a novel methodology that leverages the benefits of using deep learning software development kit (SDK) for performance optimization. In the proposed methodology, we first obtain the Pareto-optimal mapping solutions of deep learning applications using the SDK associated with the hardware platform. Afterward, we perform mapping of dataflow tasks and selection of mapping solutions of deep learning (DL) applications together through a genetic algorithm. Experiments with a real-life example and randomly generated graphs show that we could reduce at least 5% of the maximum utilization compared to our previous work that maps DL applications and dataflow applications sequentially. INDEX TERMSModel-based software design, mapping exploration, deep learning applications, heterogeneous processors.

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“…According to [15], it is not necessary to check all the jobs in the busy period, but just the initial jobs, until the following condition is true…”

Section: Analysis Of Fp Tasksmentioning

confidence: 99%

Response time analysis for tasks scheduled under EDF within fixed priorities

Harbour

Palencia

Proceedings. 2003 International Symposium on System-on-Chip (IEEE Cat. No.03EX748)

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1Hierarchical schedulers are getting increased attention in many research projects because they bring in flexibility, they can take advantage of the best features of different scheduling policies, and allow the composability of applications developed under different scheduling strategies. Most commercial real-time operating systems have an underlying fixed priority scheduler, and for this reason it is necessary to be able to analyze hierarchically-scheduled applications in which the underlying scheduler is of that kind. In this paper we extend the classic response time analysis techniques to analyze applications which can have a mixture of tasks scheduled either with fixed priorities, or with an earliest deadline first (EDF) scheduler running on top of an underlying fixed priority scheduler. We show that the complexity of this analysis is similar to that of existing response time analysis for EDF tasks.

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On the schedulability analysis for distributed hard real-time systems

Cited by 45 publications

References 8 publications

Schedulability analysis for tasks with static and dynamic offsets

Schedulability analysis for tasks with static and dynamic offsets

A Novel Technique to Support Deep Learning Applications in a Model-Based Embedded Software Design Methodology

Response time analysis for tasks scheduled under EDF within fixed priorities

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