This paper presents and tests several task scheduling methods in a real-time multitasking system based on the thread interleaving mechanism. The original configurable multicore time-predictable system architecture for multitasking is briefly discussed. The essential requirement of the system is the predictability of tasks, regardless of their number and when they are initialized. The paper focuses on the appropriate configuration of core interleaving registers, which determine the order and frequency of execution of individual tasks. This study develops various heuristic algorithms based on genetic programming and task execution rate analysis, and implements these in the Python language and PROLOG. The experiments are conducted with different task scenarios and system work requirements: with minimization of resources, energy, and operating frequency. A special task analyzer is used to evaluate the quality of the resulting system configuration. The results obtained are tested on a real hardware structure implemented in an FPGA chip. The proposed approach can be a useful tool for configuring real-time multitasking systems.
Pułka et al. (Thu,) studied this question.