TY - GEN
T1 - Semi-federated scheduling of mixed-criticality system for sporadic DAG tasks
AU - Yang, Tao
AU - Tang, Yue
AU - Jiang, Xu
AU - Deng, Qingxu
AU - Guan, Nan
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2019/5/1
Y1 - 2019/5/1
N2 - DAG task model is a general parallel task model that has been widely concerned and studied by researchers. The combination of mixed-criticality and DAG task model makes it difficult to analyze system behaviors. Under federated mixed-criticality scheduling algorithm, tasks are physically isolated with regard to computation resources, which leads to lower analysis complexity and better performance. However, federated mixed-criticality scheduling algorithm suffers resource waste as in federated scheduling, and almost half of processor resources can be wasted in extreme cases. In this paper, we address the problem and propose a novel semi-federated mixed-criticality algorithm (SFMC). SFMC combines semi-federated scheduling with mixed-criticality systems, whose original architecture is changed to a dual-hierarchical one. When analyzing the combined system, we first allocate finer-grained processor resources to each MC DAG task, then we prove the correctness of the SFMC algorithm in both normal and critical states. The proposed algorithm is evaluated on randomly generated independent DAG task sets based on OpenMP benchmarks. Experiment results present that our algorithm has better performance on schedulability than the federated mixed-criticality scheduling algorithm.
AB - DAG task model is a general parallel task model that has been widely concerned and studied by researchers. The combination of mixed-criticality and DAG task model makes it difficult to analyze system behaviors. Under federated mixed-criticality scheduling algorithm, tasks are physically isolated with regard to computation resources, which leads to lower analysis complexity and better performance. However, federated mixed-criticality scheduling algorithm suffers resource waste as in federated scheduling, and almost half of processor resources can be wasted in extreme cases. In this paper, we address the problem and propose a novel semi-federated mixed-criticality algorithm (SFMC). SFMC combines semi-federated scheduling with mixed-criticality systems, whose original architecture is changed to a dual-hierarchical one. When analyzing the combined system, we first allocate finer-grained processor resources to each MC DAG task, then we prove the correctness of the SFMC algorithm in both normal and critical states. The proposed algorithm is evaluated on randomly generated independent DAG task sets based on OpenMP benchmarks. Experiment results present that our algorithm has better performance on schedulability than the federated mixed-criticality scheduling algorithm.
KW - Mixed-criticality
KW - Parallel
KW - Virtualization
UR - https://www.scopus.com/pages/publications/85070388654
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85070388654&origin=recordpage
U2 - 10.1109/ISORC.2019.00039
DO - 10.1109/ISORC.2019.00039
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781728101507
T3 - Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019
SP - 163
EP - 170
BT - Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019
PB - IEEE
T2 - 22nd IEEE International Symposium on Real-Time Distributed Computing, ISORC 2019
Y2 - 7 May 2019 through 9 May 2019
ER -