Multiprocessor Resource Allocation for Hard-Real-Time Streaming with a Dynamic Job-Mix

Moreira, Orlando; Mol, Jan David; Bekooij, Marco; Meerbergen, J. van

doi:10.1109/rtas.2005.33

Cited by 50 publications

(56 citation statements)

References 4 publications

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“…Moreira et al have investigated real-time scheduling of dataflow programs modeled as Homogeneous SDF (HSDF) graphs in [19,20,21]. They formulated a resource allocation heuristic [19] and a TDM scheduler combined with static allocation policy [20].…”

Section: Related Workmentioning

confidence: 99%

“…They formulated a resource allocation heuristic [19] and a TDM scheduler combined with static allocation policy [20]. Their TDM scheduler improves the one proposed in [3].…”

Section: Related Workmentioning

confidence: 99%

“…Our approach differs from [3,19,20] in: 1) using the periodic task model which allows applying a variety of proven hard-realtime scheduling algorithms for multiprocessors, 2) providing direct analysis of acyclic graphs for which the maximum cycle mean analysis can not be applied without converting the graph into a cyclic one, and 3) using the CSDF model which is more expressive than SDF graphs. Compared to [21], our approach differs in: 1) we derive an equation which always finds the actors periods that ensure periodic execution and scheduling.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the existing run-time scheduling solutions assume applications modeled as task graphs and provide best-effort or soft-real-time services [22]. Few run-time scheduling solutions exist which support applications modeled using a MoC and provide hard-real-time services [10,3,19,20]. However, these solutions either use simple MoCs such as SDF/PGM graphs or use TimeDivision Multiplexing (TDM)/Round-Robin (RR) scheduling combined with heuristics.…”

Section: Introductionmentioning

confidence: 99%

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Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Bamakhrama

Stefanov

2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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Most of the hard-real-time scheduling theory for multiprocessor systems assumes independent periodic or sporadic tasks. Such a simple task model is not directly applicable to modern embedded streaming applications. This is because a modern streaming application is typically modeled as a directed graph where nodes represent actors (i.e. tasks) and edges represent data-dependencies. The actors in such graphs have data-dependency constraints and do not necessarily conform to the periodic or sporadic task models. Therefore, in this paper we investigate the applicability of hardreal-time scheduling theory for periodic tasks to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. In such graphs, the actors are data-dependent, however, we analytically prove that they (i.e. the actors) can be scheduled as implicitdeadline periodic tasks. As a result, a variety of hard-real-time scheduling algorithms for periodic tasks can be applied to schedule such applications with a certain guaranteed throughput. We compare the throughput resulting from such scheduling approach to the maximum achievable throughput of an application for a set of 19 real streaming applications. We find that in more than 80% of the cases, the throughput resulting from our approach is equal to the maximum achievable throughput.

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Section: Related Workmentioning

confidence: 99%

“…They formulated a resource allocation heuristic [19] and a TDM scheduler combined with static allocation policy [20]. Their TDM scheduler improves the one proposed in [3].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Bamakhrama

Stefanov

2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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“…To the best of our knowledge, existing execution models are either tailored to a single model of computation [6]- [12] and do not provide insights into possible tradeoffs in execution of each MoC.…”

Section: Introductionmentioning

confidence: 99%

A Unified Execution Model for Data-Driven Applications on a Composable MPSoC

Nejad

Molnos

Goossens

2011

2011 14th Euromicro Conference on Digital System Design

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Abstract-Multi-processor Systems on Chip (MPSoCs) execute multiple applications concurrently. These applications may belong to different domains, i.e., may have firm-, soft-, or non-realtime requirements. A composable system simplifies system design, integration, and verification by avoiding the inter-application interference. Existing work demonstrates composability for applications expressed using a single model of computation. For example, Kahn Process Network (KPN) and dataflow are two common data-driven parallel models of computation, each with different properties and suited for different application domains. This paper extends existing work with support for concurrent, composable execution of KPN and dataflow applications on the same MPSoC platform. We formalize a unified execution model by defining its operations that implement the different models of computation on the MPSoC, and discuss the trade-offs involved. Our experiments indicate that multiple applications modeled in KPN and dataflow run composably on an MPSoC platform.

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Scheduling Real-Time Streaming Applications onto an Embedded Multiprocessor

Moreira

Corporaal

2014

Embedded Systems

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Multiprocessor Resource Allocation for Hard-Real-Time Streaming with a Dynamic Job-Mix

Cited by 50 publications

References 4 publications

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

A Unified Execution Model for Data-Driven Applications on a Composable MPSoC

Scheduling Real-Time Streaming Applications onto an Embedded Multiprocessor

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