Computing periodic request functions to speed-up the analysis of non-cyclic task models
Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent...
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Gespeichert in:
| Autor*in: |
Zeng, Haibo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Real-time systems - Springer US, 1989, 51(2014), 4 vom: 17. Sept., Seite 360-394 |
|---|---|
| Übergeordnetes Werk: |
volume:51 ; year:2014 ; number:4 ; day:17 ; month:09 ; pages:360-394 |
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| DOI / URN: |
10.1007/s11241-014-9209-5 |
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OLC2054361011 |
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| 520 | |a Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. | ||
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10.1007/s11241-014-9209-5 doi (DE-627)OLC2054361011 (DE-He213)s11241-014-9209-5-p DE-627 ger DE-627 rakwb eng 004 VZ Zeng, Haibo verfasserin aut Computing periodic request functions to speed-up the analysis of non-cyclic task models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. Real-time schedulability Task graph model Max-plus algebra Request/Demand bound functions Di Natale, Marco aut Enthalten in Real-time systems Springer US, 1989 51(2014), 4 vom: 17. Sept., Seite 360-394 (DE-627)130955892 (DE-600)1064543-3 (DE-576)025100394 0922-6443 nnns volume:51 year:2014 number:4 day:17 month:09 pages:360-394 https://doi.org/10.1007/s11241-014-9209-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4036 GBV_ILN_4318 AR 51 2014 4 17 09 360-394 |
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10.1007/s11241-014-9209-5 doi (DE-627)OLC2054361011 (DE-He213)s11241-014-9209-5-p DE-627 ger DE-627 rakwb eng 004 VZ Zeng, Haibo verfasserin aut Computing periodic request functions to speed-up the analysis of non-cyclic task models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. Real-time schedulability Task graph model Max-plus algebra Request/Demand bound functions Di Natale, Marco aut Enthalten in Real-time systems Springer US, 1989 51(2014), 4 vom: 17. Sept., Seite 360-394 (DE-627)130955892 (DE-600)1064543-3 (DE-576)025100394 0922-6443 nnns volume:51 year:2014 number:4 day:17 month:09 pages:360-394 https://doi.org/10.1007/s11241-014-9209-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4036 GBV_ILN_4318 AR 51 2014 4 17 09 360-394 |
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10.1007/s11241-014-9209-5 doi (DE-627)OLC2054361011 (DE-He213)s11241-014-9209-5-p DE-627 ger DE-627 rakwb eng 004 VZ Zeng, Haibo verfasserin aut Computing periodic request functions to speed-up the analysis of non-cyclic task models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. Real-time schedulability Task graph model Max-plus algebra Request/Demand bound functions Di Natale, Marco aut Enthalten in Real-time systems Springer US, 1989 51(2014), 4 vom: 17. Sept., Seite 360-394 (DE-627)130955892 (DE-600)1064543-3 (DE-576)025100394 0922-6443 nnns volume:51 year:2014 number:4 day:17 month:09 pages:360-394 https://doi.org/10.1007/s11241-014-9209-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4036 GBV_ILN_4318 AR 51 2014 4 17 09 360-394 |
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Computing periodic request functions to speed-up the analysis of non-cyclic task models |
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Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. © Springer Science+Business Media New York 2014 |
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Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. © Springer Science+Business Media New York 2014 |
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Abstract Tasks are units of sequential code implementing the system actions and executed concurrently by an operating system. Techniques have been developed to determine, at design time, whether a set of tasks can safely complete before their deadlines. Several models have been proposed to represent conditional executions and dependencies among concurrent tasks for the purpose of schedulability analysis. Among them, task graphs with cyclic recurrent behavior (i.e., those modeled with a single source vertex and a period parameter specifying the minimum amount of time that must elapse between successive activations of the source job) allow for efficient schedulability analysis based on the periodicity of the request and demand bound functions (rbf and dbf). In this paper, we leverage results from max-plus algebra to identify a recurrent term in rbf and dbf of general task graph models, even when the execution is neither recurrent nor controlled by a period parameter. As such, the asymptotic complexity of calculating rbf and dbf is independent from the length of the time interval. Experimental results demonstrate significant improvements on the runtime for system schedulability analysis. © Springer Science+Business Media New York 2014 |
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Computing periodic request functions to speed-up the analysis of non-cyclic task models |
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https://doi.org/10.1007/s11241-014-9209-5 |
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Di Natale, Marco |
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