1. Preemptive Behavior Analysis and Improvement of Priority Scheduling Algorithms Xiaoying Wang Northeastern University China

2. 1.Introduction 2. Micro scheduling model of ready queue 3. Case study and performance evaluation 4. Conclusion

3.  This Research was supported by National Natural Science Foundation of China grant by 60203011.

4. Most of embedded real-time systems only deploy necessary resources so that the extra preemption overheads among tasks debase the system performance terribly. Through scheduling process analysis of periodic task, this paper presents the waiting limit formula of each task in ready queue while guarantees its deadline.

5. In addition, some properties such as final preempt time is deduced and the necessary condition of preemptive behavior of periodic tasks is quantitatively described. Based on them, a micro scheduling preempt model for periodic tasks in ready queue is put forward, which decreases preempt amount and optimizes system performance through change preempt relationship.

6. The model cannot only decreases preempt amount effectively but enhances processor utilization for static priority scheduling algorithm such as rate monotonic scheduling, which is demonstrated by case study and simulation.

7. Symbol Definition n is the total number of tasks in task set; Q is the ready queue; τ i is the identification of task, 1≤i≤n; C i is the worst case computation time of task τ i, 1≤i≤n; T i is the period of task τ i, 1≤i≤n; D i is the deadline of task τ i, 1≤i≤n;

8. Symbol Definition P i is the priority of task τ i, a smaller value of P i denotes a higher priority, 1≤i≤n; W i is the accumulate waiting time of task τ i in ready queue, namely the accumulate suspend time, 1≤i≤n; R i is the accumulate executing time of task τ i in ready queue, 1≤i≤n; F i is the phase of task τ i, 1≤i≤n.

9. Example Task Set

10. Example Sequence of RM Scheduling

11. Attribute of Time 21

12. Attribute of Time 17

13. Theorem For a give task set of n periodic schedulable tasks, if the K th execution of the lowest priority task τ n postpones ℓ nk time and can meet its deadline, then the K+1 th execution of task τ n can still meet its deadline.

14. Lemma For a give task set of n periodic schedulable tasks, if the K th execution of task τ i postpones ℓ ik time and can meet its deadline, then the K+1 th execution of task τ i can still meet its deadline.

15. Case study and performance evaluation Optimization on static priority scheduling Optimization on dynamic priority scheduling

16. RM Sequence Adapting Micro Scheduling Algorithm EDF Sequence Adapting Micro Scheduling Algorithm Sequence of EDF Scheduling

17. Simulation Comparison

18. Conclusion The model can not only decrease preempt amount and reduce overheads of real-time systems to a great extent, but improve processor schedulable utilization of static priority scheduling algorithm as well.

19. Future work Compute task’s optimum phase offline through optimization algorithm The longest postponement time of preemption for soft deadline tasks

20. Thank you for your consideration!