4.8 Article

Flow Scheduling for Conflict-Free Network Updates in Time-Sensitive Software-Defined Networks

期刊

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 3, 页码 1668-1678

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2998224

关键词

Schedules; Job shop scheduling; Informatics; Real-time systems; Processor scheduling; Network topology; Integer linear programming (ILP)-based scheduling; network updates; time-sensitive networking (TSN); time-triggered (TT) networks

资金

  1. NSFC Program [U19A2062, U1801263]
  2. China Postdoctoral Science Foundation [2019M660439]
  3. Key-Area Research and Development Program of Guangdong Province [2020B010164001]

向作者/读者索取更多资源

The digital transformation of industry requires highly flexible and deterministic industrial control networks. The new paradigm of time-sensitive software-defined networking combines the features of time-sensitive networking and software-defined networking. This article proposes a flow schedule generation model that ensures no data loss without extra update overhead, with two algorithms designed to adapt to different application scenarios.
The digital transformation of industry requires industrial control networks provide high flexibility and determinacy. Time-sensitive software-defined networking that combines time-sensitive networking and software-defined networking is a new network paradigm which provides both real-time transmission feature and network flexibility. During network updates, the transmission consistency needs to be maintained. However, previous mechanisms mostly target on the proper schedule transition, which cannot guarantee no frame loss and also introduces extra update overhead. The article proposes a novel flow schedule generation model which guarantees no frame loss during network updates even with the basic two-phase update mechanism and introduces no extra update overhead. Two algorithms are designed for the model to adapt to different application scenarios: the offline algorithm poses better schedulability, whereas the online one consumes less time with slightly decreased schedulability. The experiments on two real-world industrial networks demonstrate our mechanism achieves zero frame loss without extra update overhead compared to existing methods, and the online algorithm saves 40% execution time with at most 10% schedulability decrease when the bandwidth utilization is less than 50%.

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