Synthesis of automotive architectures is a complex problem that needs an automated support. AUTOSAR, standard for the specification of automotive architectures, defines a synthesis process of software components and their connections in a set of fixedpriority OS tasks distributed over a network of ECUs. During the synthesis process software components are allocated on ECUs. Since each component encapsulates a set of so-called runnable entities, synthesis completes by partitioning runnable entities in OS tasks with assigned fixed priorities. This paper proposes an optimization approach for the synthesis of AUTOSAR architectures based on genetic algorithms and mixed integer linear programming techniques. Optimization criteria consider end-to-end timing responses and memory consumption.

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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.