TASK OPTIMISATION IN MULTIPROCESSOR EMBEDDED SYSTEMS

In this work, a task execution optimization method using replication in a multiprocessor system has been developed. This method effectively minimizes the overall execution time, ensures load balancing, and reduces communication delays. A key feature of the method is the implementation of task migration according to replication principles, utilizing an optimization objective function. The conducted experiment with the system demonstrated that the selected optimization method efficiently balances the load; however, additional objective functions are required for energy consumption optimization. Simulation results show that increasing the number of processors reduces the maximum load and the number of migrations, while an increase in the number of tasks raises the system load and the number of migrations at the initial stages. The migration mechanism effectively balances the load, particularly in the early execution phases. Future research directions include refining embedded device classifications and detailing their characteristics. For each class of embedded devices, it will be necessary to adapt task optimization algorithms and methods, as well as develop appropriate optimization objective functions.