Quantitative analysis of real-time performance and hardware requirements for edge computing platform

For real-time edge systems such as autonomous driving,not only the correctness of task functions,but also the response and processing time of tasks should be satisfied.In the hardware selection phase of a real-time system,time series analyses must be performed on the hardware platform running real-time applications.At present,the common method of worst-case execution time(WCET)analysis focuses mainly on analyzing the impact of hardware platform architecture or task execution process on the task running time.However,different tasks in an autopilot system have different levels of urgency,and preemption between tasks is the main factor that affects the task execution time.The key problem is how to quantify the time fluctuation caused by task preemption for each subtask of the autopilot system running on a fixed hardware platform.This paper presents a time analysis method for a real-time application based on a queuing theory and preemptive scheduling strategy,which assigns different priorities to tasks according to their time urgency and preemptive scheduling according to task priority.Through an experimental case study,the impact of the running time of each subtask in a real-time application with task priority preemptive scheduling is analyzed,along with the impact of changes in hardware platform performance on such real-time applications.