Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems
In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks i...
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MDPI AG
2022-06-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/11/7/101 |
| _version_ | 1797406943376048128 |
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| author | Behnaz Ranjbar Hamidreza Alikhani Bardia Safaei Alireza Ejlali Akash Kumar |
| author_facet | Behnaz Ranjbar Hamidreza Alikhani Bardia Safaei Alireza Ejlali Akash Kumar |
| author_sort | Behnaz Ranjbar |
| collection | DOAJ |
| description | In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works. |
| first_indexed | 2024-03-09T03:34:04Z |
| format | Article |
| id | doaj.art-f5a6a090445742a48dfc1eeeea271cc7 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T03:34:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-f5a6a090445742a48dfc1eeeea271cc72023-12-03T14:51:54ZengMDPI AGComputers2073-431X2022-06-0111710110.3390/computers11070101Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality SystemsBehnaz Ranjbar0Hamidreza Alikhani1Bardia Safaei2Alireza Ejlali3Akash Kumar4CFAED, Technische Universität (TU) Dresden, 01069 Dresden, GermanyDepartment of Computer Engineering, Sharif University of Technology, Tehran 11365-11155, IranDepartment of Computer Engineering, Sharif University of Technology, Tehran 11365-11155, IranDepartment of Computer Engineering, Sharif University of Technology, Tehran 11365-11155, IranCFAED, Technische Universität (TU) Dresden, 01069 Dresden, GermanyIn Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works.https://www.mdpi.com/2073-431X/11/7/101drop ratedynamic slackMachine-LearningMixed-CriticalityQuality-of-Service (QoS)run-time management |
| spellingShingle | Behnaz Ranjbar Hamidreza Alikhani Bardia Safaei Alireza Ejlali Akash Kumar Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems Computers drop rate dynamic slack Machine-Learning Mixed-Criticality Quality-of-Service (QoS) run-time management |
| title | Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems |
| title_full | Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems |
| title_fullStr | Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems |
| title_full_unstemmed | Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems |
| title_short | Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems |
| title_sort | learning oriented qos and drop aware task scheduling for mixed criticality systems |
| topic | drop rate dynamic slack Machine-Learning Mixed-Criticality Quality-of-Service (QoS) run-time management |
| url | https://www.mdpi.com/2073-431X/11/7/101 |
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