A graphical modeling tool supporting automated schedule synthesis for time-sensitive networking

• Published in: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) pp. 1 - 8
• Main Authors: Farzaneh, Morteza Hashemi, Kugele, Stefan, Knoll, Alois
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Knowledge based systems; Logic programming; Manufacturing automation; Network topology; Object oriented modeling; Quality of service; Real-time systems; Schedules; Timing
• ISSN: 1946-0759
• DOI: 10.1109/ETFA.2017.8247599

Time-Sensitive Networking (TSN) is a set of new standards which is being developed by the Institute of Electrical and Electronics Engineers to support mixed-criticality requirements based on Ethernet technology. These standards have recently raised the attention of real-time domains such as automation and automotive. To support tight timing guarantees, Time-Aware Shaper (IEEE 802.1Qbv) is introduced based on the theory of time-triggered communication. However, the configuration of Time-Aware Shaper requires expertise and is a time consuming procedure. We aim to automate this procedure reducing configuration overhead. A novel graphical modeling is introduced which combines the strengths of model-based and logic programming modeling paradigms. Using the graphical editor, network topology, dataflow based on a publisher and subscriber concept, and the quality of service requirements are specified. Facts for a network knowledge base are automatically derived from the model. This knowledge base is used to generate constraints for schedule synthesis. These constraints are solved using a Satisfiability Modulo Theories solver to find a correct schedule. Moreover, we exploit the solver's capability of producing unsatisfiable cores and use it for network model correction. We annotated all generated schedule constraints and mapped them to the stream names to track the unsatisfiable streams from the solver's output. We gained insightful results showing that this information significantly helps to correct an unsatisfiable network model to find a feasible schedule.