Optimized Allocation of Fault-tolerant Embedded Software with End-to-end Timing Constraints

It is desirable to optimize power consumption of distributed safety-critical soft- ware that realize fault tolerance and maximize reliability as a result, to support the increasing complexity of software functionality in safety-critical embedded systems. Likewise, safety-critical applications that are required to meet end- to-end timing constraints may require additional computing resources. In this paper, we propose a scalable software-to-hardware allocation based on hybrid particle-swarm optimization with hill-climbing and differential algorithms to efficiently map software components to a network of heterogeneous computing nodes while meeting the timing and reliability constraints. The approach assumes fixed-priority preemptive scheduling, and delay analysis that value fresh- ness of data, which is typical in control software applications.Our proposed solution is evaluated on a range of software applications, which are synthesized from a real-world automotive AUTOSAR benchmark. The evaluation makes comparative analysis of the different algorithms, and a solution based on integer-linear programming, which is an exact method. The results show that the hybrid with the hill-climbing algorithms return very close solutions to the exact method and outperformed the hybrid with the differential algorithm, though consumes more time. The hybrid with the stochastic hill- climbing algorithm scales better and its optimality can be deemed acceptable.