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Pinwheel Scheduling for Power-Aware Real-Time Systems Gaurav Chitroda Komal Kasat Nalini Kumar

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

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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 3

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

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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 5

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Slack Time Slack time of a job with deadline d i at any time t such that t < d i is ( d i –t) Job 1Job 2Job 3Job 4 deadline job1: d1deadline job 2 : d2 Slack time (job1) Slack time (job 2) Scheduling interval at time t0 Scheduling interval at time d1 6

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DVS & DFS Power dynamic = C L * N SW * V 2 dd * f C L : Load Capacitance V dd: supply voltage N SW : avg no of ckt switches per clock cycle f: clock frequency These techniques are applied meeting real time constraints T1T2T3T4T5 T1T4 T3 0 t1 t2 t3 t4 t5 t6 time frequencyfrequency T5T2T1T3T4 7

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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 2P i -e i or as small as e i J ij Period P i J ij+1 J ij+2 Period P i 2P i – e i e i time 8

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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} T1T2T3T4T5T1T2T3T4T5 9

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

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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. 11

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T1T2T3T4T5T1T2T3T4T5 T1T2T3T4T5T1T2T3T4T5 0 5.3 10.6 15.9 Rate Monotonic Scheduling without Pinwheel. Periods are {9.2, 10.6, 10.7, 21.4, 23.4} After Pinwheel Scheduling. The Periods are {5.3, 10.6, 10.6, 21.2, 21.2} Start of T 1s period. So T 3 is halted Start of T 1s period, so T 5 halted Start of T 1s period. So T 3 is halted T 2 s period started, but T1 yet not finished so T 2 is delayed. 12

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Power-Aware Algorithm Using Pinwheel model Offline Scheduling- Apriori knowledge of realtime jobs including periods,execution times,release times,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. 13

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Pinwheel Transformation Process Power- Aware real-time Scheduling using Pinwheel model can be divided into two phases- 1) Generate and store Pinwheel schedules. All task periods are transformed to harmonic integers. 2) 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. 14

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

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Key Idea- To manage slack times in order to reduce energy dissipation. Under the restriction that no task misses its deadline- 1) We can lower the processor voltage and frequency to reduce energy consumption. 2) We can use a simple Heuristic to know slack times in advance. This only partially solves the problem. Three methods used in the paper- 1) Low Power Fixed Priority Scheduling(LPFPS) 2) Greedy Method 3) Linear Programming(LP method) Methods of Power Reduction 16

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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 17

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Example : T1T1 T2T2 T1T1 T2T2 T3T3 T5T5 T4T4 T3T3 T4T4 T5T5 Slack Time T 1 and T 3 finish earlier then their WCETs, but only T 2 and T 5 execute using lower processor frequency to make use of the slack times. T 4 executes using the maximum frequency because it is not the only one task left in the ready queue. T2T2 T5T5 time frequency RM Scheduling LPFPS using pinwheel 18

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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 19

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Greedy Method Example : T1T1 T2T2 T3T3 Slack time wasted The first ready task gets scheduled to maximize slack time utilization T1T1 T2T2 T3T3 T1T1 Pinwheel time frequency Greedy Method 20

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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 21

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

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LP Method - Constraints 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 23

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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 24

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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 25

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

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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 28

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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 RESULTS 29

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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. 30

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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 31

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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 Scheduling Overheads 32

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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 33

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

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

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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. 36

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QUESTIONS 37

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PP Test Review Sections 6-1 to 6-6

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