Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

被引:44
|
作者
Jin, Xi [1 ,2 ,3 ]
Xia, Chongqing [1 ,2 ,3 ]
Guan, Nan [4 ]
Zeng, Peng [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Key Lab Networked Control Syst, Shenyang 110016, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Peoples R China
[3] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Peoples R China
[4] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Message fragmentation; networked control system; real-time scheduling; time sensitive network;
D O I
10.1109/JAS.2021.1003844
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 networks (TSNs) support not only traditional best-effort communications but also deterministic communications, which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints. No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications. However, due to inappropriate message fragmentation, the real-time performance of no-wait scheduling algorithms is reduced. Therefore, in this paper, joint algorithms of message fragmentation and no-wait scheduling are proposed. First, a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions. Second, to improve the scalability of our algorithm, the worst-case delay of messages is analyzed, and then, based on the analysis, a heuristic algorithm is proposed to construct low-delay schedules. Finally, we conduct extensive test cases to evaluate our proposed algorithms. The evaluation results indicate that, compared to existing algorithms, the proposed joint algorithm improves schedulability by up to 50%.
引用
收藏
页码:478 / 490
页数:13
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