MSS: Exploiting Mapping Score for CQF Start Time Planning in Time-Sensitive Networking

被引:19
作者
Guo, Miao [1 ]
Gu, Chaojie [1 ]
He, Shibo [1 ]
Shi, Zhiguo [1 ]
Chen, Jiming [1 ]
机构
[1] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
基金
中国国家自然科学基金;
关键词
Cyclic queuing and forwarding (CQF) model; industrial Internet of Things (IIoT); scheduling; time-sensitive networking; mapping score;
D O I
10.1109/TII.2022.3206815
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Time-sensitive networking (TSN), an emerging network technology, requires high-performance scheduling mechanisms to deliver deterministic service in Industry 5.0. Cyclic queuing and forwarding (CQF) is launched to simplify the configuration complexity of the early stage mechanism time-aware shaper in TSN flow scheduling. Previous CQF studies adopt an inflexible incremental flow scheduling scheme, which consists of flow sorting, offset search, and resource judgment. However, we observe that flow sorting and offset search are mutually interdependent. The offset of a flow helps determine the resource status on the flow path, which can guide flow sorting. By utilizing the interaction between flow and offset, we design a novel scheduling approach that achieves high scheduling performance and time efficiency. Specifically, the proposed approach combines flow sorting and offset search together to select flow and its offset (i.e., (flow, offset)) simultaneously. To effectively determine the selecting priority and select the potential optimal flow-offset combination, we define a unified metric, $mapping\,score$, to quantify the schedulability of different flow and offset combinations. The extensive experiments demonstrate that the scheduling success rate of our proposed approach is on average 31.69% higher than the baseline and 4.57% higher than the state-of-the-art flow judgement approach (FLJ) method. Moreover, it outperforms the state-of-art FLJ method by 7.62% in large-scale linear topologies, indicating its great scalability in different network scales and complex topologies.
引用
收藏
页码:2140 / 2150
页数:11
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