An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling
Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems w...
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| Autor*in: |
Zhao, Yecheng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Real-time systems - Springer US, 1989, 53(2017), 4 vom: 18. Jan., Seite 467-525 |
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| Übergeordnetes Werk: |
volume:53 ; year:2017 ; number:4 ; day:18 ; month:01 ; pages:467-525 |
| Links: |
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| DOI / URN: |
10.1007/s11241-017-9267-6 |
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OLC2054361526 |
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| 520 | |a Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). | ||
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10.1007/s11241-017-9267-6 doi (DE-627)OLC2054361526 (DE-He213)s11241-017-9267-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhao, Yecheng verfasserin aut An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). Mixed-criticality systems Adaptive mixed-criticality scheduling Schedulability analysis Design optimization Zeng, Haibo (orcid)0000-0003-1162-759X aut Enthalten in Real-time systems Springer US, 1989 53(2017), 4 vom: 18. Jan., Seite 467-525 (DE-627)130955892 (DE-600)1064543-3 (DE-576)025100394 0922-6443 nnns volume:53 year:2017 number:4 day:18 month:01 pages:467-525 https://doi.org/10.1007/s11241-017-9267-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4036 AR 53 2017 4 18 01 467-525 |
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10.1007/s11241-017-9267-6 doi (DE-627)OLC2054361526 (DE-He213)s11241-017-9267-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhao, Yecheng verfasserin aut An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). Mixed-criticality systems Adaptive mixed-criticality scheduling Schedulability analysis Design optimization Zeng, Haibo (orcid)0000-0003-1162-759X aut Enthalten in Real-time systems Springer US, 1989 53(2017), 4 vom: 18. Jan., Seite 467-525 (DE-627)130955892 (DE-600)1064543-3 (DE-576)025100394 0922-6443 nnns volume:53 year:2017 number:4 day:18 month:01 pages:467-525 https://doi.org/10.1007/s11241-017-9267-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4036 AR 53 2017 4 18 01 467-525 |
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10.1007/s11241-017-9267-6 doi (DE-627)OLC2054361526 (DE-He213)s11241-017-9267-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zhao, Yecheng verfasserin aut An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). Mixed-criticality systems Adaptive mixed-criticality scheduling Schedulability analysis Design optimization Zeng, Haibo (orcid)0000-0003-1162-759X aut Enthalten in Real-time systems Springer US, 1989 53(2017), 4 vom: 18. Jan., Seite 467-525 (DE-627)130955892 (DE-600)1064543-3 (DE-576)025100394 0922-6443 nnns volume:53 year:2017 number:4 day:18 month:01 pages:467-525 https://doi.org/10.1007/s11241-017-9267-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4036 AR 53 2017 4 18 01 467-525 |
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Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). © Springer Science+Business Media New York 2017 |
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Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). © Springer Science+Business Media New York 2017 |
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Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. Experimental results show that our analysis provides much better scalability for optimization procedures, with only small loss of performance (less than 7% in weighted schedulability, and no more than 4% in optimization objectives). © Springer Science+Business Media New York 2017 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2054361526</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504062506.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11241-017-9267-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2054361526</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11241-017-9267-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhao, Yecheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In the design optimization of real-time systems, the schedulability analysis is used to define the feasibility region within which tasks meet their deadlines, so that optimization algorithms can find the best solution within the region. However, the current analysis techniques for systems with adaptive mixed-criticality (AMC) scheduling are based on response time calculation, which are too complex for optimization purposes. In this paper, we provide a simpler schedulability test based on request bound functions, which allows an efficient definition of the feasibility region for AMC. We prove that the new analysis is safe with bounded pessimism. 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