Industrial control systems are distributed real-time applications deployed on field and controller devices, connected by fieldbuses. Their lack of interoperability, mutually and towards IT-systems, is a major obstacle...
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Industrial control systems are distributed real-time applications deployed on field and controller devices, connected by fieldbuses. Their lack of interoperability, mutually and towards IT-systems, is a major obstacle for the digitalization. Converged networks are considered as the solution across industries with time-Sensitive Networking (TSN) as the enabling technology. To ease the adoption of TSN for industrial automation, a modular framework was developed for future engineering tools. The core of the framework consists of a module management including interfaces and a YANG-based datastore, maintaining a system-wide data model. Modules are provided for network configuration implementing CUC-functions, monitoring and container-based application and deployment.
Recent development methodologies from the industry and the academia for complex real-time systems define a stage in which system functions are deployed onto an execution platform. The deployment consists of the placem...
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Recent development methodologies from the industry and the academia for complex real-time systems define a stage in which system functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. In this paper, we present two approaches towards the efficient deployment of realistic and complex real-time systems by considering tree-shaped functional models. A formal approach to compute optimal deployment and a heuristic approach to scale to industry-size systems. The approaches consider placement, partitioning and scheduling, and are based on mixed integer linear programming (MILP) technique. Furthermore, we present a deep evaluation of the proposed deployment approaches to show the benefits and limits of a MILP-based deployment approach. A set of synthetic use-cases as well as a real-life automotive system are used to assess the quality and scalability of our deployment approaches. Considering use-cases, we show an added value with respect to end-to-end latencies optimization when solving the three stages of the deployment problem at the same time. This is done by comparing the quality of the solutions obtained with our techniques to those returned by the existing approaches.
Modern development methodologies from the industry and the academia for complex real-time systems define a stage in which application functions are deployed onto an execution platform. The deployment consists of the p...
详细信息
ISBN:
(纸本)9781450320856
Modern development methodologies from the industry and the academia for complex real-time systems define a stage in which application functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. None of the existing optimization techniques deal with the three stages of the deployment problem at the same time. In this paper, we present a staged approach towards the efficient deployment of real-time functions based on genetic algorithms and mixed integer linear programming techniques. Application to case studies shows the applicability of the method to industry-size systems and the quality of the obtained solutions when compared to the true optimum for small size examples.
Modern development methodologies from the industry and the academia for complex real-time systems define a stage in which application functions are deployed onto an execution platform. The deployment consists of the p...
详细信息
ISBN:
(纸本)9781450320856
Modern development methodologies from the industry and the academia for complex real-time systems define a stage in which application functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. None of the existing optimization techniques deal with the three stages of the deployment problem at the same time. In this paper, we present a staged approach towards the efficient deployment of real-time functions based on genetic algorithms and mixed integer linear programming techniques. Application to case studies shows the applicability of the method to industry-size systems and the quality of the obtained solutions when compared to the true optimum for small size examples.
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