Load - aware intelligent multiprocessor scheduler for time-critical cyber-physical system applications

Abstract

The cyber-physical system involves domains like physical, communication, and cyber system. The time-critical tasks in the physical domain demand a quick computational response from the cyber system. To meet such critical response times, the cyber system uses a multiprocessor computing system to execute the task. But the performance of such a computing system depends on the multiprocessor task scheduler and load-state of the multiprocessor. If the scheduler is not aware of the load-state of the multiprocessor, the scheduling overloads the particular set of processors, and the time-critical task-sets are not able to meet their deadline. To overcome this issue, this paper presents a load-aware multiprocessor scheduling mechanism. The proposed scheduler uses a machine-learning algorithm to efficiently predict the load to make prior awareness of the load and schedule. The simulation results prove that the algorithm outperforms in comparison with the classical multiprocessor scheduler.