Multiplexing Adaptive with Classic AUTOSAR? Adaptive Software Control to Increase Resource Utilization in Mixed-Critical Systems

International audienceAutomotive embedded systems need to cope with antagonist requirements: on the one hand, the users and market pressure push car manufacturers to integrate more and more services that go far beyond the control of the car itself. On the other hand, recent standardization efforts in the safety domain has led to the development of the ISO 26262 norm that defines means and requirements to ensure the safe operation of automotive embedded systems. In particular, it led to the definition of ASIL (Automotive Safety and Integrity Levels), i.e., it formally defines several criticality levels. Handling the increased complexity of new services makes new architectures, such as multi or many-cores, appealing choices for the car industry. Yet, these architectures provide a very low level of timing predictability due to shared resources, which goes in contradiction with timing guarantees required by ISO 26262. For highest criticality level tasks, Worst-Case Execution Time analysis (WCET) is required to guarantee that timing constraints are respected. The WCET analyzers consider the worst-case scenario: whenever a critical task accesses a shared resource in a multi/many-core platform, a WCET analyzer considers that all cores use the same resource concurrently. To improve the system performance, we proposed in a earlier work an approach where a critical task can be run in parallel with less critical tasks, as long as the real-time constraints are met. When no further interferences can be tolerated, the proposed run-time control suspends the low critical tasks until the termination of the critical task. In an automotive context, the approach can be translated as a highly critical partition, namely a classic AUTOSAR one, that runs on one dedicated core, with several cores running less critical Adaptive AUTOSAR application(s). We briefly describe the design of our proven-correct approach. Our strategy is based on a graph grammar to formally model the critical task as a set of control flow graphs on which a safe partial WCET analysis is applied and used at run-time to control the safe execution of the critical task