An optimization approach for the synthesis of AUTOSAR architectures

Abstract

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.

A holistic optimization approach for the synthesis of AUTOSAR E/E architecture

“…There are mainly two recent and relevant works in this context [5,6]. In [5], authors tackle the synthesis problem of AUTOSAR architecture using genetic algorithm.…”

“…In [5], authors tackle the synthesis problem of AUTOSAR architecture using genetic algorithm. They proposed an integrated framework to address the problems of mapping SWC to ECU, runnable to task, and data to network signal.…”

Parcus: Energy-Aware and Robust Parallelization of AUTOSAR Legacy Applications

“…Hence, meta-heuristics, such as simulated annealing [14,15] or EAs [16] are popular as they scale well. Generally, the PSQ can be used as decision criterion in any heuristic.…”

Minimizing a real-time task set through Task Clustering

“…They consider implicit deadlines and fixed task-priority scheduling. In the same context, authors of [38] formulate the task clustering problem as an optimization problem. Authors present a first technique based on mixed integer linear programming (MILP) and a second one based on the genetic algorithms (GA) to optimize end-to-end responses and memory consumption.…”