Deadline Miss Rate Scheduling in DAG Considering Probabilistic Execution Time Article Swipe
Hayate Toba
,
Daichi Yamazaki
,
Takuya Azumi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3606480
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3606480
Autonomous driving systems have been extensively researched for safety. To ensure safety, a strict deadline is enforced before sensor data influences vehicle control. A common approach to this issue is Directed Acyclic Graph (DAG) scheduling. However, existing studies use fixed values, typically using Worst-case Execution Time (WCET). During actual vehicle operation, the task execution time varies due to factors such as input size (e.g., surrounding vehicles), and the actual execution time often takes on a value smaller than the WCET. The execution time variation causes the actual results of DAG scheduling to deviate from the intended strategy. To address this problem, this paper proposes a DAG scheduling algorithm that considers probabilistic execution time to reduce the number of deadline misses. For consecutive timer-driven nodes, a waiting time may occur, during which executing higher priority nodes does not affect end-to-end deadline misses. The proposed scheduling algorithm calculates this waiting time and executes high deadline miss rate nodes outside the end-to-end path during this time. In DAGs with high utilization, the evaluation results of the proposed scheduling algorithm demonstrate that the number of deadline misses decreases compared to existing methods. Specifically, our evaluation results indicate an approximate 8.3% improvement in deadline achievement rate compared to existing methods in high utilization scenarios.
Related Topics
Concepts
Computer science
Probabilistic logic
Scheduling (production processes)
Processor scheduling
Earliest deadline first scheduling
Parallel computing
Real-time computing
Dynamic priority scheduling
Distributed computing
Rate-monotonic scheduling
Mathematical optimization
Operating system
Schedule
Artificial intelligence
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3606480
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414008318
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https://openalex.org/W4414008318Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3606480Digital Object Identifier
- Title
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Deadline Miss Rate Scheduling in DAG Considering Probabilistic Execution TimeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Hayate Toba, Daichi Yamazaki, Takuya AzumiList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3606480Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2025.3606480Direct OA link when available
- Concepts
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Computer science, Probabilistic logic, Scheduling (production processes), Processor scheduling, Earliest deadline first scheduling, Parallel computing, Real-time computing, Dynamic priority scheduling, Distributed computing, Rate-monotonic scheduling, Mathematical optimization, Operating system, Schedule, Artificial intelligence, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 88, 117, 171, 180 |
| abstract_inverted_index.on | 73 |
| abstract_inverted_index.to | 26, 57, 91, 113, 185, 202 |
| abstract_inverted_index.DAG | 89, 105 |
| abstract_inverted_index.For | 120 |
| abstract_inverted_index.The | 80, 141 |
| abstract_inverted_index.and | 66, 149 |
| abstract_inverted_index.due | 56 |
| abstract_inverted_index.for | 7 |
| abstract_inverted_index.may | 127 |
| abstract_inverted_index.not | 136 |
| abstract_inverted_index.our | 189 |
| abstract_inverted_index.the | 51, 67, 78, 85, 94, 115, 157, 168, 172, 178 |
| abstract_inverted_index.use | 38 |
| abstract_inverted_index.8.3% | 195 |
| abstract_inverted_index.DAGs | 164 |
| abstract_inverted_index.Time | 45 |
| abstract_inverted_index.been | 4 |
| abstract_inverted_index.data | 19 |
| abstract_inverted_index.does | 135 |
| abstract_inverted_index.from | 93 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.high | 151, 166, 206 |
| abstract_inverted_index.miss | 153 |
| abstract_inverted_index.path | 159 |
| abstract_inverted_index.rate | 154, 200 |
| abstract_inverted_index.size | 62 |
| abstract_inverted_index.such | 59 |
| abstract_inverted_index.task | 52 |
| abstract_inverted_index.than | 77 |
| abstract_inverted_index.that | 108, 177 |
| abstract_inverted_index.this | 27, 99, 101, 146, 161 |
| abstract_inverted_index.time | 54, 70, 82, 112, 126, 148 |
| abstract_inverted_index.with | 165 |
| abstract_inverted_index.(DAG) | 33 |
| abstract_inverted_index.Graph | 32 |
| abstract_inverted_index.WCET. | 79 |
| abstract_inverted_index.fixed | 39 |
| abstract_inverted_index.input | 61 |
| abstract_inverted_index.issue | 28 |
| abstract_inverted_index.nodes | 134, 155 |
| abstract_inverted_index.often | 71 |
| abstract_inverted_index.paper | 102 |
| abstract_inverted_index.takes | 72 |
| abstract_inverted_index.time. | 162 |
| abstract_inverted_index.using | 42 |
| abstract_inverted_index.value | 75 |
| abstract_inverted_index.which | 130 |
| abstract_inverted_index.(e.g., | 63 |
| abstract_inverted_index.During | 47 |
| abstract_inverted_index.actual | 48, 68, 86 |
| abstract_inverted_index.affect | 137 |
| abstract_inverted_index.before | 17 |
| abstract_inverted_index.causes | 84 |
| abstract_inverted_index.common | 24 |
| abstract_inverted_index.during | 129, 160 |
| abstract_inverted_index.ensure | 10 |
| abstract_inverted_index.higher | 132 |
| abstract_inverted_index.misses | 182 |
| abstract_inverted_index.nodes, | 123 |
| abstract_inverted_index.number | 116, 179 |
| abstract_inverted_index.occur, | 128 |
| abstract_inverted_index.reduce | 114 |
| abstract_inverted_index.sensor | 18 |
| abstract_inverted_index.strict | 13 |
| abstract_inverted_index.varies | 55 |
| abstract_inverted_index.(WCET). | 46 |
| abstract_inverted_index.Acyclic | 31 |
| abstract_inverted_index.address | 98 |
| abstract_inverted_index.deviate | 92 |
| abstract_inverted_index.driving | 1 |
| abstract_inverted_index.factors | 58 |
| abstract_inverted_index.methods | 204 |
| abstract_inverted_index.misses. | 119, 140 |
| abstract_inverted_index.outside | 156 |
| abstract_inverted_index.results | 87, 170, 191 |
| abstract_inverted_index.safety, | 11 |
| abstract_inverted_index.safety. | 8 |
| abstract_inverted_index.smaller | 76 |
| abstract_inverted_index.studies | 37 |
| abstract_inverted_index.systems | 2 |
| abstract_inverted_index.values, | 40 |
| abstract_inverted_index.vehicle | 21, 49 |
| abstract_inverted_index.waiting | 125, 147 |
| abstract_inverted_index.Directed | 30 |
| abstract_inverted_index.However, | 35 |
| abstract_inverted_index.approach | 25 |
| abstract_inverted_index.compared | 184, 201 |
| abstract_inverted_index.control. | 22 |
| abstract_inverted_index.deadline | 14, 118, 139, 152, 181, 198 |
| abstract_inverted_index.enforced | 16 |
| abstract_inverted_index.executes | 150 |
| abstract_inverted_index.existing | 36, 186, 203 |
| abstract_inverted_index.indicate | 192 |
| abstract_inverted_index.intended | 95 |
| abstract_inverted_index.methods. | 187 |
| abstract_inverted_index.priority | 133 |
| abstract_inverted_index.problem, | 100 |
| abstract_inverted_index.proposed | 142, 173 |
| abstract_inverted_index.proposes | 103 |
| abstract_inverted_index.Execution | 44 |
| abstract_inverted_index.algorithm | 107, 144, 175 |
| abstract_inverted_index.considers | 109 |
| abstract_inverted_index.decreases | 183 |
| abstract_inverted_index.executing | 131 |
| abstract_inverted_index.execution | 53, 69, 81, 111 |
| abstract_inverted_index.strategy. | 96 |
| abstract_inverted_index.typically | 41 |
| abstract_inverted_index.variation | 83 |
| abstract_inverted_index.Autonomous | 0 |
| abstract_inverted_index.Worst-case | 43 |
| abstract_inverted_index.calculates | 145 |
| abstract_inverted_index.end-to-end | 138, 158 |
| abstract_inverted_index.evaluation | 169, 190 |
| abstract_inverted_index.influences | 20 |
| abstract_inverted_index.operation, | 50 |
| abstract_inverted_index.researched | 6 |
| abstract_inverted_index.scenarios. | 208 |
| abstract_inverted_index.scheduling | 90, 106, 143, 174 |
| abstract_inverted_index.vehicles), | 65 |
| abstract_inverted_index.achievement | 199 |
| abstract_inverted_index.approximate | 194 |
| abstract_inverted_index.consecutive | 121 |
| abstract_inverted_index.demonstrate | 176 |
| abstract_inverted_index.extensively | 5 |
| abstract_inverted_index.improvement | 196 |
| abstract_inverted_index.scheduling. | 34 |
| abstract_inverted_index.surrounding | 64 |
| abstract_inverted_index.utilization | 207 |
| abstract_inverted_index.timer-driven | 122 |
| abstract_inverted_index.utilization, | 167 |
| abstract_inverted_index.Specifically, | 188 |
| abstract_inverted_index.probabilistic | 110 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.53921428 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |