CSC 714: Final Project Report

A task scheduling simulator for timing analysis of the various task sets with different scheduling policies was implemented and tested. The simulator supports the various scheduling policies like Rate Monotonic, deadline monotonic and EDF, widely used for scheduling real time applications. It simulates the task execution for the time period, and then it outputs the time based representation of the scheduling of set of inputs tasks. It also calculates the optimal preemption threshold values for the task sets, and uses it to prevent the unnecessary preemptions. Motivation Scheduling real‐time tasks is complicated work since it requires pre‐computed information about the task's timing properties. Although we know the system's timing properties, scheduling tasks is highly concentrated mental work. Moreover the system designer is responsible to see that system meets real time constraints. For guaranteeing feasibility of generated scheduling, several static and dynamic priority scheduling algorithms and corresponding schedulability tests are introduced such as Rate‐Monotonic (RM), Deadline‐Monotonic (DM) and Earliest Deadline First (EDF). For developing a system with real time constraints, the preemptability is considered a necessary requirement. But in reality, there is execution overhead associated with the preemptions. And as the number of tasks in task set are increased, the run time overheads increases exponentially. The Preemption Threshold Scheduling (PTS) is suggested to help fixed‐priority scheduling to improve the overall system's timing performance by removing unwanted preemptions [2]. The preemption threshold brings one more priority, preemption threshold, to the system. While the regular priority is used when scheduling the task, preemption threshold is used when being preempted by another task. Therefore while meeting task's deadline, we reduce useless preemption, which frequently occurs with traditional fixed priority scheduling. PTS slightly lengthen the execution time of higher tasks, but it reduces that of lower tasks. This helps enhancing the response time and utilization of overall system. Also it improves the scalability of the system.