Event monitoring for adaptive multi-priority streaming time sensitive-based EDF scheduling

Abstract

Real-time systems are bounded with strict time constraints. To accomplish this, task scheduling is needed. Earlier approaches are restricted to fixed priority scheduling policies, which follows static priority algorithm. It assigns a priority statically and schedules dynamically. It does not support dynamic priority requests. To overcome this, preemptive earliest deadline first (EDF) scheduling is used, which is a dynamic priority scheduling algorithm. It ensures that higher priority requests are executed first and they experience lower mean waiting time, without leading lower priority requests to overstarvation. But preemptive EDF leads to increase in runtime overhead. Hence, proposed method uses limited preemption EDF scheduling, which assigns an approximate deadline for each request, and the requests are serviced with limited preemption. It splits the request into multiple jobs and assigns fixed preemption points (FPP) to each sub job. Only at FPP position, preemption is allowed. Hence, it is proved experimentally that the mean waiting time for higher and lower priority tasks are the minimum with less runtime overhead.