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Extended Gantt-chart in real-time scheduling for single processor

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Presentation on theme: "Extended Gantt-chart in real-time scheduling for single processor"— Presentation transcript:

1 Extended Gantt-chart in real-time scheduling for single processor
Scheduling Tool 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 Power Time Starting time Ending time Power level Energy consumption Demo

2 Scheduling chart for multi-processor and multiple power consumers
Scheduling Tool 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 A B C D Constant task A Periodic task B Periodic task C Task D follows B Power Time Demo

3 Scheduling Tool 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 A B C D Power Time 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 Demo

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

5 Example – Mars Rover System specification Power supply
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

6 Scheduling Example – Mars Rover
Timing constraints

7 Scheduling Method Constraint graph construction Resource specification
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

8 System health check / Thc System health check / Thc
Constraint Graph Hazard detection / Thd System health check / Thc Heat steer 1 / Ths Heat steer 2 / Ths Heat steer 3 / Ths Heat steer 4 / Ths Steer / Ts thc -(thc + Thc) -ths System health check / Thc Heat wheel 1 / Thw Heat wheel 2 / Thw Heat wheel 3 / Thw Heat wheel 4 / Thw Heat wheel 5 / Thw Heat wheel 6 / Thw Drive / Td - thw

9 Resource Specification
Hazard detection (C) / Thc / Phc_C Health check (C) / Thc / Phc_C Heat steer i (C) / Ths_C / Phs_C Heat steer i (T) / Ths_T / Phs_T Heat wheel j (C) / Thw_C / Phw_C Heat wheel j (T) / Thw_T / Phw_T Steer (C) / Ts_C / Ps_C Steer (M) / Ts_M / Ps_M Drive (C) / Td_C / Pd_C Drive (M) / Td_M / Pd_M Computation Mechanical Thermal Heat steer i Heat wheel j Health check Steer Drive Hazard detection -ths + Ths_E -thw + Thw_E thc -(thc + Thc)

10 Scheduling Hazard detection (C) / Thc / Phc_C
Heat steer i (C) / Ths_E / Phs_E Heat steer i (T) / Ths_T / Phs_T Heat wheel j (C) / Thw_E / Phw_E Heat wheel j (T) / Thw_T / Phw_T Steer (C) / Ts_C / Ps_C Steer (M) / Ts_M / Ps_M Drive (C) / Td_C / Pd_C Drive (M) / Td_M / Pd_M -ths + Ths_E -thw Primary resource: Computation Auxiliary resource: Mechanical Auxiliary resource: Thermal Health check (C) / Thc / Phc_C thc -(thc + Thc) -ths -thw + Thw_E -Ts_C + Ts_M

11 Scheduling Example – Mars Rover
Power constraints Different solar power supply over time Different power consumption over temperature/time

12 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 System heart-beat - moving two steps (a) Begin with health check (b) no health check

13 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 System heart-beat - moving two steps (a) Begin with health check (b) no health check

14 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 System heart-beat - moving two steps (a) Begin with health check (b) no health check

15 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 System heart-beat - moving two steps (a) Begin with health check (b) no health check

16 Comparison Existing solution Our 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

17 Evaluation What is the value of our tool? How to evaluate the tool?
Shorter execution time Is this valuable? More energy consumption from battery Is this bad? How to evaluate the tool? Application level evaluation Heart-beat is not the correct level to evaluate Map schedules to applications Power related scenario Various power constraint (supply/consumption) over different stages of application Power-aware adaptive scheduling for different stages Evaluate overall performance of the whole mission

18 Application-level Evaluation
Mission description Target location – 48 steps from current location Power condition 14.9W for first 10 minutes, 12W for next 10 minutes, 9W thereafter Performance metrics Execution time Total energy drown from battery

19 Application-level Evaluation
Power-awareness Execution speed scales with power condition adaptively Smart schedule Maximize best case Avoid worst case Tradeoff: cost vs. efficiency Use energy wisely Application-specific Application-level knowledge Working mode parameters of components

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