1. 1 Scheduling Mapping of tasks to time slots  Computation  Communication Mapping of power usage to time slots  Mechanical devices  Thermal subsystems  Other electronics subsystems Constraints  Real-time deadlines, periods, min/max separation  Power budget, power surge (min/max)  Potentially scenario-driven

2. 2 Scheduling techniques Deadline based real-time scheduling on multiprocessors  Rate-monotonic scheduling – extend existing RM scheduling to multiprocessors  Timing constraint graph scheduling – multiple serializable sequences in a single heart beat

3. 3 Novel IMPACCT scheduler A novel graphical tool  Timing and power constraint visualization  Transforms them into graph problems  Give designers a vision to the power surge at run-time Complete system-level model  All power sources  All power consumers Power-aware scheduling  Schedule operations based on power source output  Both performance requirement and power constraint  Regulate power surge  Optimize for power efficiency and reduce execution time

4. 4 Power Time Starting timeEnding time Power levelEnergy consumption Demo IMPACCT scheduler Extended Gantt-chart in real-time scheduling for single processor  Event – bins Timing – horizontal size Power – vertical size Energy – area of the bin  Power surge – compacting bins downward

5. 5 A BBBB C CCC C DDD Constant task A Periodic task B Periodic task C Task D follows B Power Time Demo IMPACCT scheduler Scheduling chart for multi-processor and multiple power consumers  Events can overlap vertically Multi-processor Multiple power consumer – electronics, mechanical, thermal  Power awareness – min and max power supply

6. 6 A B C D Power Time B C Deadline of B (scheduling space) Deadline of B Min timing constraint of D Max timing constraint of D Deadline of C (scheduling space) Deadline of C Scheduling space of D Slide bin within timing space Squeeze/extend bin to available time slot C C Demo IMPACCT scheduler Timing constraints – bin packing problem to satisfy horizontal constraints  Independent tasks – moving bins horizontally  Dependent tasks – moving grouped bins horizontally  Power/voltage/clock scaling – extending/squeezing bins

7. 7 A B C D Power Time B Manual scheduling while monitoring power surge C A B C D Power Time B Attack spike Automated global scheduling to meet min-max power CC Max Min Improve utilization Demo IMPACCT scheduler Power constraints – bin packing problem to satisfy vertical constraints  Automatic optimization – let the tool do everything  Manual optimization – visualizing power in manual scheduling

8. 8 Example revisited – Mars Rover System specification  6 wheel motors  4 steering motors  System health check  Hazard detection Power supply  Battery (non-rechargeable)  Solar panel Power consumption  Digital Computation, imaging, communication, control  Mechanical Driving, steering  Thermal Motors must be heated in low-temperature environment

9. 9 Timing constraints – Mars Rover

10. 10 Scheduling method Constraint graph construction  Nodes: operations  Edges: precedence relationship between operations Resource specification  Resource: an executing unit that can perform operations independently Six thermal resources for wheel heating Four thermal resources for steer motor heating One mechanical resource for driving One mechanical resource for steering One computation resource for control  Operations on one resource must be serialized Scheduling  Primary resource selection  Schedule primary resource by applying graph algorithms  Auxiliary resources and power requirement are considered as scheduling constraints

11. 11 Constraint graph System health check / T hc t hc -(t hc + T hc ) Heat wheel 1 / T hw Heat wheel 2 / T hw Heat wheel 3 / T hw Heat wheel 4 / T hw Heat wheel 5 / T hw Heat wheel 6 / T hw Heat steer 2 / T hs Heat steer 3 / T hs Heat steer 4 / T hs Hazard detection / T hd Steer / T s Drive / T d - t hw -t hs Heat steer 1 / T hs

12. 12 -t hs + T hs_E -t hw + T hw_E t hc -(t hc + T hc ) Resource specification Hazard detection (C) / T hc / P hc_C Health check (C) / T hc / P hc_C Heat steer i (C) / T hs_C / P hs_C Heat steer i (T) / T hs_T / P hs_T Heat wheel j (C) / T hw_C / P hw_C Heat wheel j (T) / T hw_T / P hw_T Steer (C) / T s_C / P s_C Steer (M) / T s_M / P s_M Drive (C) / T d_C / P d_C Drive (M) / T d_M / P d_M Health check (C) / T hc / P hc_C Computation Mechanical Thermal Heat steer i Heat wheel j Health check Steer Drive Hazard detection

13. 13 Scheduling graph Hazard detection (C) / T hc / P hc_C Heat steer i (C) / T hs_E / P hs_E Heat steer i (T) / T hs_T / P hs_T Heat wheel j (C) / T hw_E / P hw_E Heat wheel j (T) / T hw_T / P hw_T Steer (C) / T s_C / P s_C Steer (M) / T s_M / P s_M Drive (C) / T d_C / P d_C Drive (M) / T d_M / P d_M -t hs + T hs_E -t hw Primary resource: Computation Auxiliary resource: Mechanical Auxiliary resource: Thermal Health check (C) / T hc / P hc_C t hc -(t hc + T hc ) -t hs -t hw + T hw_E -T s_C + T s_M

14. 14 Example – Mars Rover Power constraints  Different solar power supply over time  Different power consumption over temperature/time

15. 15 System heart-beat - moving two steps (a) Begin with health check (b) no health check Previous solution by JPL Over-constrained, conservative  Serialize every operation to satisfy power constraint  Longer execution time and under-utilization of solar power  No scheduling tool is used – manual scheduling Not power-aware  Scheduling without considering power sources and consumers

16. 16 System heart-beat - moving two steps (a) Begin with health check (b) no health check Solution 1: high solar power (14.9W) Max solar power: 14.9W at noon  Improved utilization of solar power  Automated scheduling – use scheduling tools Aggressive – do as much as possible  heating motors while doing other operations  Fastest moving speed – no waiting on heating

17. 17 System heart-beat - moving two steps (a) Begin with health check (b) no health check Solution 2: typical solar power (12W) Moderate solar power output – 12W  Improved utilization of solar power  Automated scheduling – use scheduling tools Moderately aggressive – avoid exceeding power limit  Relaxed constraint –heating motors while doing other operations  Faster moving speed – some waiting time on heating

18. 18 System heart-beat - moving two steps (a) Begin with health check (b) no health check Solution 3: low solar power (9W) Minimum solar power output – 9W  Restricted constraint – serialize operations  Automated scheduling – use scheduling tools Conservative – same as JPL solution  Slow moving speed  Full utilization of low solar power

19. 19 Comparison JPL's previous solution  Conservative – long execution time, low solar power utilization  Not power aware – same schedule for all cases  Not intend to use battery energy Our solution  Adaptive – speedup when solar power supply is high  Power-aware – smart scheduling on different power supply/consumption  Use battery energy when necessary

20. 20 Application-level evaluation Mission description  Target location – 48 (distance-) steps away from current location Power condition  14.9W solar power for first 10 minutes, 12W for next 10 minutes, 9W thereafter Metrics  Execution time  Total energy drawn from battery

21. 21 Application-level evaluation Power-awareness  Execution speed scales with power condition adaptively Smart schedule  Maximize best case  Avoid worst case Tradeoff  Power vs. performance  Energy renewability Application-specific  Application-level knowledge  Working mode parameters of components