Efficient Task-Network Scheduling With Task Conflict Metric in Time-Sensitive Networking

With the rapid development of Industrial Internet of Things (IIoT), time-sensitive networking (TSN) with deterministic and real-time features has gained broad interest. However, most existing research focuses on the network scheduling with fixed task placement and computing resource allocation, rest...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2024-02, Vol.20 (2), p.1528-1538
Main Authors: Xu, Lei, Xu, Qimin, Chen, Cailian, Zhang, Yanzhou, Wang, Shouliang, Guan, Xinping
Format: Article
Language:English
Subjects:
Algorithms
Industrial applications
Industrial development
Industrial Internet of Things
Internet of Things
Job shop scheduling
Parallel scheduling
Placement
Processor scheduling
Real-time systems
Resource allocation
Resource scheduling
Routing
Scheduling
Task analysis
Task scheduling
task-conflict metric (TCM)
task-network scheduling (TNS)
time-sensitive networking (TSN)
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Summary:With the rapid development of Industrial Internet of Things (IIoT), time-sensitive networking (TSN) with deterministic and real-time features has gained broad interest. However, most existing research focuses on the network scheduling with fixed task placement and computing resource allocation, restricting the scheduling space of coupled task-network. To tackle this coupling problem, an efficient task-network scheduling (ETNS) scheme is proposed in this article for TSN. A task-conflict metric (TCM) is established to quantify the competition degree of scheduling resources. For increasing the overall scheduling space, a TCM-aware prescheduling method is proposed by optimizing task placement and routing paths to reduce the potential conflicts between tasks. Integrated with the prescheduling method, we design a TCM-aware parallel group-scheduling algorithm by reducing the conflicts between task groups to enhance schedulability and scalability. Experiments show that our ETNS scheme significantly improves the schedulability and scalability performances compared with the existing scheduling approaches. The larger the number of tasks, the higher the performance improvement.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2023.3278883