Feedback Control for Real - time Scheduling ’

Most of real-time scheduling algorithms are open-loop algorithms as the scheduling decisions are based on the worst-case estimates of task parameters. They do not continuously observe the performance of the system and do not dynamically adjust the system parameters t o improve performance. In many cases, it is preferable to base scheduling decisions on average-case workload parameters and be ready to deal with bounded transient overloads dynamically. In recent years, the “closed-loop” scheduling has gained importance due to its applicability to many real-world problems wherein the feedback information can be exploited efficiently to adjust task and/or scheduler parameters, thereby improving the system‘s performance. In this paper, we discuss an open-loop dynamic scheduling algorithm that employs a notion of task overlap in the scheduler in order to provide some flexibility in task execution time. Then we present a novel closed-loop approach for dynamically estimating the execution time of tasks based on both deadline miss ratio and task rejection ratio in the system. This approach is highly preferable for firm/soft real-time systems since it provides a firm performance guarantee in terms of deadline misses while achieving a high guarantee ratio. We design proportional-integral controller arid H-infinity controller for closed loop scheduling. We evaluate the performance of the open-loop and the closed-loop approaches using simulation studies. We show that the closed-loop dynamic scheduling offers a better performance over the open-loop scheduling under all practical conditions.