Monotonicity and run-time scheduling

Wiggers, Maarten H.; Bekooij, Marco; Smit, Gerard J. M.

doi:10.1145/1629335.1629359

Cited by 32 publications

(83 citation statements)

References 31 publications

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“…To ensure that a dataflow model is temporally conservative to a task graph, sufficient conditions on the relation between such a dataflow model and a corresponding task graph are presented in [10]. This means that the temporal analysis results (minimum throughput and maximum latency) calculated using the dataflow model are also valid for the corresponding task graph.…”

Section: B Conservative Temporal Analysismentioning

confidence: 99%

“…This means that the temporal analysis results (minimum throughput and maximum latency) calculated using the dataflow model are also valid for the corresponding task graph. The remainder of this subsection summarizes these results from [10]. The sufficient conditions are based on a one-toone correspondence between the task graph and the dataflow model.…”

Section: B Conservative Temporal Analysismentioning

confidence: 99%

“…As shown in [10], when proving that the dataflow component is temporally conservative to the corresponding task it is sufficient to prove that when the enabling time of firing i of the dataflow model is later or equal to the enabling time of execution i of the task, also the finish time of firing i is later or equal to the finish time of execution i of the task, i.e.…”

Section: Examplementioning

confidence: 99%

“…RELATED WORK While [10] introduced a modeling relation between dataflow components and tasks to analyze the effects of runtime scheduling, state-of-the-art in modeling the task workload function is to have a one-to-one correspondence between dataflow firings and task executions. Each firing has a duration equal to an upper bound on the corresponding task execution time [10]- [13]. Our method does not have this restrictive oneto-one relation between firings and executions, instead we introduce a method that uses the modeling relation between dataflow components and tasks to capture the tasks workload functions.…”

Section: Introductionmentioning

confidence: 99%

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Two parameter workload characterization for improved dataflow analysis accuracy

Hausmans

Geuns

Wiggers

et al. 2013

2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Abstract-Real-time stream processing applications, such as radios, can often be modeled intuitively with dataflow models. Given the Worst-Case Execution Times (WCETs) of the tasks, which characterizes workload with one parameter, dataflow analysis techniques have been used to compute the minimum throughput and maximum latency of these applications. However, a large difference between the WCETs of the tasks and their average execution times can result in a large difference between the computed worst-case throughput and the actual obtained throughput.To reduce the difference between the worst-case throughput, determined by analysis, and the actual obtained throughput, we introduce in this paper a two parameter (σ, ρ) workload characterization of the tasks to improve the accuracy of dataflow analysis. The (σ, ρ) workload characterization captures information on the maximum cumulative execution time of consecutive executions of a task and can therefore be seen as a generalization of the WCET characterization.We show how the (σ, ρ) workload characterization can be used in combination with several types of dataflow graphs and how it can be used to improve the temporal analysis results of real-time stream processing applications. We illustrate this for a DVB-T radio application, a car-radio application and a data-dependent MP3 playback application.

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Section: B Conservative Temporal Analysismentioning

confidence: 99%

Section: B Conservative Temporal Analysismentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two parameter workload characterization for improved dataflow analysis accuracy

Hausmans

Geuns

Wiggers

et al. 2013

2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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“…Dataflow applications traditionally use static scheduling techniques, i.e. Time Division Multiple Access (TDMA) [2], [4]- [8], however, they have recently been shown to work well also with real-time scheduling techniques [9]- [12], run-time budget schedulers [13], [14] and non-starvation-free schedulers [15].…”

Section: Introductionmentioning

confidence: 99%

Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

Ali

Åkesson

Pinho

2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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Many embedded multi-core systems incorporate both dataflow applications with timing constraints and traditional real-time applications. Applying real-time scheduling techniques on such systems provides real-time guarantees that all running applications will execute safely without violating their deadlines. However, to apply traditional realtime scheduling techniques on such mixed systems, a unified model to represent both types of applications running on the system is required. Several earlier works have addressed this problem and solutions have been proposed that address acyclic graphs, implicit-deadline models or are able to extract timing parameters considering specific scheduling algorithms.In this paper, we present an algorithm for extracting real-time parameters (offsets, deadlines and periods) that are independent of the schedulability analysis, other applications running in the system, and the specific platform. The proposed algorithm: 1) enables applying traditional real-time schedulers and analysis techniques on cyclic or acyclic Homogeneous Synchronous Dataflow (HSDF) applications with periodic sources, 2) captures overlapping iterations, which is a main characteristic of the execution of dataflow applications, 3) provides a method to assign offsets and individual deadlines for HSDF actors, and 4) is compatible with widely used deadline assignment techniques, such as NORM and PURE. The paper proves the correctness of the proposed algorithm through formal proofs and examples. Abstract-Many embedded multi-core systems incorporate both dataflow applications with timing constraints and traditional real-time applications. Applying real-time scheduling techniques on such systems provides real-time guarantees that all running applications will execute safely without violating their deadlines. However, to apply traditional real-time scheduling techniques on such mixed systems, a unified model to represent both types of applications running on the system is required. Several earlier works have addressed this problem and solutions have been proposed that address acyclic graphs, implicit-deadline models or are able to extract timing parameters considering specific scheduling algorithms.In this paper, we present an algorithm for extracting real-time parameters (offsets, deadlines and periods) that are independent of the schedulability analysis, other applications running in the system, and the specific platform. The proposed algorithm: 1) enables applying traditional real-time schedulers and analysis techniques on cyclic or acyclic Homogeneous Synchronous Dataflow (HSDF) applications with periodic sources, 2) captures overlapping iterations, which is a main characteristic of the execution of dataflow applications, 3) provides a method to assign offsets and individual deadlines for HSDF actors, and 4) is compatible with widely used deadline assignment techniques, such as NORM and PURE. The paper proves the correctness of the proposed algorithm through formal proofs and examples.

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