1. Probabilistic Preemption Control using Frequency Scaling for Sporadic Real-time Tasks Abhilash Thekkilakattil, Radu Dobrin and Sasikumar Punnekkat

2. Real-time Systems 2 web images

3. 3 Real-time tasks Mapping of Events to Real-time Tasks web images

4. Sporadic Real-time Tasks 4 job1job2 Worst Case Execution Time Min. inter-arrival time (Period) Release time Relative deadline task release probability 0.05 0.06 0.07 0.16 0.25 0.17 0.09 0.07 012345678 time Probabilistic task arrivals Task worst case execution time scales with processor frequency

5. Real-time Scheduling  Guarantee task completions before their deadline 5 Non-preemptive Scheduling Low runtime overhead  Increased blocking times Preemptive Scheduling Ability to achieve high utilization  Preemption costs high priority low priority preemption cost blocking high priority low priority

6. 6 Common Preemption Related Costs Scheduler related cost – Overhead involved in saving and retrieving the context of the preempted task Cache related preemption delays – Overhead involved in reloading cache lines – Vary with the point of preemption – Increased bus contention Pipeline related cost – Clear the pipeline upon preemption – Refill the pipeline upon resumption

7. Frequency Scaling in Real-time Systems 7 CPU frequency: Task takes less time to execute at higher frequencies  changes the schedule behavior  requires higher voltages P: Power consumption C: Effective capacitance V: Applied voltage F: CPU frequency Processor power consumption: minmax CPU frequency

8. What? 8 We examine the use of CPU frequency scaling to control preemption behavior in sporadic real-time task systems with probabilistic task releases.

9. Related work 9 Need for preemption elimination recognized Ramamritham-94, Burns-95, Ramaprasad-06 RM- more preemptions than EDF Buttazzo-05 Attribute re-assignment for FPS for preemption control Dobrin-04 Preemption aware scheduling algorithms Yang-09, Woonseok -04 Preemption threshold Scheduling Lamie-97, Saksena-00, Wang-99 Context switches and cache related preemption delays Lee-98, Schneider-00, Katcher-93 DVS: energy conservation- increase in preemptions Pillai-01,Aydin-04, Bini-09, Marinoni-07 Limited preemption scheduling Baruah-05, Bertogna -10

10. Methodology Overview 10 Offline phase Online phase inputs

11. Offline Phase Derive permitted relaxation to inter-arrival times  Minimum inter-arrival times: worst case scenario Determines task set schedulability  Relax the release times of jobs: using probabilities Most probable release time after the minimum inter-arrival time has elapsed Apply this relaxation at runtime for preemption control Permits for trade-offs 11

12. Online Phase Online preemption control algorithm: At runtime, find the earliest probable time instant of preemption Speed-up the busy period to avoid preemption Speed-up to maximum speed if preemption cannot be avoided - To keep the simplicity of the online algorithm 12

13. 13 Example 13 TaskComputation time Inter-arrival time A15 B37 C320 5 10 5 5 A B C Original Fixed Priority Schedule

14. Offline Phase 14 TaskComputation time Time period Relaxation to min. inter- arrival times for threshold probability =0.20 Relaxation to min. inter- arrival times for threshold probability =0.24 A1513 B3713 C32013 0 1 2 3 4 5 6 7 8 9 10 0.04 0.08 0.24 0.20 0.02 0.04 0.08 0.1 0.08 (time) Task release probability

15. Online Phase 15 TaskComputation time Inter-arrival time Relaxation A151 B371 C3201 5 10 5 5 A B C C=1+3+3 = 7 t=6 (since we use release time probabilities) Speed=7/6  We speed-up to max. speed: simplicity earliest possible preemption point C=3 t=1 Speed=3/1

16. Evaluation 16 Processor Speed 012345 Power consumption per clock cycle (mW) 02050 200500 No. of task sets1400 No. of tasks per task set 3-15 Algorithm usedUUniFast* LCM≤ 2000 Threshold probabilities 0.20 and 0.24 * E. Bini and G. C. Buttazzo, “Measuring the performance of schedulability tests,” Real-Time Systems, 2005. 0 1 2 3 4 5 6 7 8 9 10 0.04 0.08 0.24 0.20 0.02 0.04 0.08 0.1 0.08 (time) Task release probability

17. Average Number of Preemptions 17 probability=0probability=0.20probability=0.24

18. Average Power Consumption 18 probability=0probability=0.20probability=0.24

19. No. of preemptions vs Energy 19 probability=0probability=0.20probability=0.24 probability=0probability=0.20probability=0.24

20. Conclusions  Manipulate energy usage to control preemptive behavior in real-time schedules  Trade-offs : energy vs. number of preemption  Simplicity of the runtime algorithm: O(n) complexity  Combined offline-online method: possibility to use more complex offline methods  Effective: significant preemption reduction shown in simulations  Limitations: increased energy consumption 20

21. Future work  Resource augmentation for preemption control  Processor speed-up for limited preemption scheduling (floating non-preemptive region scheduling)  Bounds on the speed-up required to guarantee a required preemption behavior  Contracts for preemption control using CPU frequency scaling  Contract definition and negotiation in Component Based Real- time Systems 21

22. Thank you ! 22 Questions ?