Pinwheel Scheduling for Power-Aware Real-Time Systems

1. Pinwheel Scheduling for Power-Aware Real-Time Systems GauravChitroda KomalKasat Nalini Kumar

2. OUTLINE Motivation Introduction Pinwheel Transformation Methods of Power Reduction Simulations and Results Conclusion

3. Motivation • Energy consumption is critical • Demand on performance and computation constantly increase energy consumption • Maintaining high performance along with increased battery life is a challenge • Using DVS and DFS to reduce Power Consumption • Online vs Offline Scheduling • Power-Aware Real -Time Scheduling

4. OUTLINE Motivation Introduction Pinwheel Transformation Methods of Power Reduction Simulations and Results Conclusion

5. Introduction • Deadlines for tasks and Slack Time • Dynamic Voltage Scaling(DVS) • Dynamic Frequency Scaling(DFS) • Distance Constrained Task Systems(DCTS) • Rate Monotonic Scheduling(RM) • Pinwheel Algorithm • Benefits

6. Slack Time • Slack time of a job with deadline diat any time t such that t < di is (di–t) deadline job1: d1 deadline job 2 : d2 Scheduling interval at time t0 Scheduling interval at time d1 Job 1 Job 2 Job 3 Job 4 Slack time (job1) Slack time (job 2)

7. DVS & DFS • Powerdynamic = CL * NSW * V2dd * f CL: Load Capacitance Vdd: supply voltage NSW: avg no of ckt switches per clock cycle f: clock frequency These techniques are applied meeting real time constraints T1 T2 T3 T4 T5 frequency T1 T2 T3 T4 T5 T3 T1 T4 0 t1 t2 t3 t4 t5 t6 time

8. DCTS - Distance Constrained Task System • Distance between 2 consecutive execution of a task should be less than a predefined value • Example: • The distance of two consecutive jobs can be as large as 2Pi-ei or as small as ei Period Pi Period Pi J ij+1 J ij+2 J ij time 2Pi – eiei

9. Rate Monotonic Scheduling • Tasks are assigned Priority based on their Periods. • Task with lowest period is assigned highest priority. • Consider a system with Five Tasks with Distance Constraints {9.2,10.6,10.7,21.4,23.4} respectively and their execution times are {1.5,2.0,3.4,1.4, 3.0} T1 T2 T3 T4 T5

10. OUTLINE Motivation Introduction Pinwheel Transformation Methods of Power Reduction Simulations and Results Conclusion

11. Pinwheel Transformation • Distance-constrained specialization Technique to transform all task distance constraints to Harmonic numbers. • Tasks can be scheduled as periodic tasks using the new distance constraint as their periods. • As the distance constraints are Harmonic numbers, the execution schedule has no jitter and meets the distance constraints. • The system predictability is increased and thus complexity of Power-Aware Real-time Scheduling can be reduced.

12. Rate Monotonic Scheduling without Pinwheel. Periods are {9.2, 10.6, 10.7, 21.4, 23.4} T1 T2 T3 T4 T5 T2’s period started, but T1 yet not finished so T2 is delayed. Start of T1’s period, so T5 halted After Pinwheel Scheduling. The Periods are {5.3, 10.6, 10.6, 21.2, 21.2} T1 T2 T3 T4 T5 Start of T1’s period. So T3 is halted Start of T1’s period. So T3 is halted Start of T1’s period, so T5 halted 0 5.3 10.6 15.9

13. Power-Aware Algorithm Using Pinwheel model • Offline Scheduling- Apriori knowledge of realtime jobs including periods,executiontimes,releasetimes,etc. • Online Vs Offline Scheduling • Benefits obtained from Pinwheel model- • Tasks information can be known apriori • Pinwheel schedule can be generated in Polynomial time and space • The rescheduling points within the hyperperiod can be massively reduced.

14. Pinwheel Transformation Process • Power- Aware real-time Scheduling using Pinwheel model can be divided into two phases- • Generate and store Pinwheel schedules. All task periods are transformed to harmonic integers. • Second Phase- Schedulers perform more precise power-aware scheduling according to their policies based on timing information obtained from the pinwheel schedule. Then DVS and DFS is dynamically performed at every rescheduling point to reduce energy consumption.

15. OUTLINE Motivation Introduction Pinwheel Transformation Methods of Power Reduction Simulations and Results Conclusion

16. Methods of Power Reduction Key Idea- To manage slack times in order to reduce energy dissipation. Under the restriction that no task misses its deadline- • We can lower the processor voltage and frequency to reduce energy consumption. • We can use a simple Heuristic to know slack times in advance. This only partially solves the problem. Three methods used in the paper- • Low Power Fixed Priority Scheduling(LPFPS) • Greedy Method • Linear Programming(LP method)

17. Low Power Fixed Priority Scheduling • Schedules using RM when there is not more than one task in the ready queue. • DVS and DFS are then applied if possible • When there are no tasks in the ready queue, the system enters Sleep mode

18. Example: Slack Time T1 T2 T3 T4 T5 frequency time RM Scheduling T1 T2 T3 T4 T5 frequency T2 T5 LPFPS using pinwheel time T1 and T3 finish earlier then their WCETs, but only T2 and T 5 execute using lower processor frequency to make use of the slack times. T4 executes using the maximum frequency because it is not the only one task left in the ready queue.

19. Greedy Method • LPFPS extended to perform DVS & DFS at every rescheduling point • Next ready job greedily uses up idle time within its scheduling interval • Processor frequency is lowered as much as possible • Reschedules at every rescheduling point to determine processor voltage and frequency • Some slack time may be wasted as remainder not sufficient for adjustments

20. Greedy Method Example: T1 T2 T3 frequency time Pinwheel T1 T2 T3 frequency T1 time Greedy Method Slack time wasted The first ready task gets scheduled to maximize slack time utilization

21. LP Method • We want to minimize the slack time • Processor frequencies are not continuous • Problem mapped to Integer Linear Programming - ILP problem – NP Complete • Heuristic used is Linear Programming – LP • LP is applied at every scheduling interval to utilize maximum slack time

22. LP Method for I = 1,2,…..m • For n jobs, with scheduling interval T • q: frequency [q1: minimum & qm: maximum] C: execution times [C1 to Cn ] • Determine processor frequency of each job in the scheduling interval • Ratio of lengthened task execution time when processor frequency reduced from qm to qi is given as X: • Find n real numbers X1, X2…… Xn such that the summation is maximum

23. LP Method - Constraints for i =1,2,….n for i =1,2,….n for i =1,2,….n for i =1,2,….n (1) (2) (3) (4) • LP Method uses all slack times for energy saving • Obtains more energy reduction than Greedy Method

24. Adaptive WCET to AET • Optimized energy consumption not obtained using WCET • Use a profiling tool • insert codes which issue rescheduling system call to update WCET • AET can be obtained from the updated WCET • Further energy saving for applications which do not use their WCET • This approach maximizes system energy reduction

25. Issue with Pinwheel Model • New periods may be shorter than the original periods • Job execution too frequent leading to change in behavior of the task • Some applications cannot accept this • Example: Video system A frame may be repeated every 22ms instead of 33ms resulting in fast forward effect • Solution: Make task idle and use this as slack time for energy saving

27. OUTLINE Motivation Introduction Pinwheel Transformation Methods of Power Reduction Simulations and Results Conclusion

28. Simulation Results • Transmeta processors – TM55EL-667 and TM58EX-933 for practical results • System utilization of 10% to 70% • 140000 test sets • Each test set has 2 to 8 real-time tasks

29. RESULTS • RM scheduling energy consumption is taken as the base • As system utilization increases, system slack time decreases • As load increases, it becomes difficult to obtain energy savings

30. RESULTS • LP methods give better energy savings than greedy methods • Average reduction of 37% to 56% at 70% utilization • More reduction on TM58EL-933 than on TM58EX-667 • More frequency steps available to adjust CPU frequencies.

31. Scheduling Overheads • RM, LPFPS, ccRM and Greedy method – little time to schedule and constant overhead • LP method – more time to schedule; and average overhead of 53us – large overhead is unacceptable

32. Scheduling Overheads • Small hyperperiod – reduces the number of rescheduling points • Overall overlap of LP method using pinwheel method is reduced due to lesser number of scheduling points

33. Effect of Profiling Tool • More energy can be saved according to task execution at runtime • Rescheduling when the codes inserted by the profiling tool are executed • Parameter of interest – AET / WCET • Bigger ratio, more saving – greater opportunity for adjustment • 33% additional energy saving at 50% AET/WCET at 70% utilization • For AET close to WCET, there is very little unused time for online schedule adjustment

34. Energy Reduction at Runtime TM 58EL-667 Average energy saving of 17.85% TM58EX-993

35. OUTLINE Motivation Introduction Pinwheel Transformation Methods of Power Reduction Simulations and Results Conclusion

36. Conclusion • Harmonic nature of pinwheel model is beneficial for deterministic task scheduling in power-aware real-time scheduling • Various techniques can be used to fully utilize whole system slack times • Power-aware scheduling with Pinwheel method achieves considerable energy savings with manageable scheduling overhead • Profiling tool provides runtime information for better scheduling • In summary : Pinwheel model is a systematic approach and a computationally feasible solution for full utilization of system slack times to minimize energy consumption.

37. QUESTIONS