Transcript DARPA Meeting - University of California, Irvine

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
Power level
Starting time
Energy
consumption
Ending time
Demo
Time
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
Power
Task D follows B
D
Periodic task C
Periodic task B
C
B
D
C
D
C
B
Constant task A
C
B
C
B
A
Time
Demo
Scheduling Tool
constraints – bin packing
problem to satisfy horizontal constraints
 Timing
tasks – moving bins horizontally
 Dependent tasks – moving grouped bins horizontally
 Power/voltage/clock scaling – extending/squeezing bins
 Independent
Power
Slide bin
within timing
space
Min timing
constraint of D
C
C
D
C
Max timing
constraint of D
Scheduling
space of D
Squeeze/extend
bin to available
time slot
C
B
B
A
Deadline of B (scheduling
space)
Deadline of B
Deadline of C (scheduling
space)
Deadline of C
Demo
Time
Scheduling Tool
constraints – bin packing problem
to satisfy vertical constraints
 Power
optimization – let the tool do everything
 Manual optimization – visualizing power in manual scheduling
 Automatic
Attack
spike
Power
Automated global
scheduling to meet
min-max power
Improve
utilization
C
Max
B
B
C
Min
D
C
A
Time
Power
C
Manual scheduling
while monitoring
power surge
D
B
C
B
A
Time
Demo
Example – 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
Scheduling Example – Mars Rover
Timing constraints
Operation
Health check
Heating steering motors
Heating wheel motors
Hazard detection
Steering
Driving
Duration Timing constrains
10 s
5s
5s
10 s
5s
10 s
Once in every 10-minute interval
Before steering
Before driving
ALL four steering motors must be heated
during the 50-second period prior to
steering.
ALL six wheel motors must be heated
during the 50-second period prior to
driving.
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
Constraint Graph
Hazard
detection / Thd
System
health check /
Thc
thc
Heat
steer
1 / Ths
Heat
steer
2 / Ths
Heat
steer
3 / Ths
Heat
steer
4 / Ths
Steer / Ts
-ths
-(thc + Thc)
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
- thw
Drive / Td
Resource Specification
Hazard
Hazard
detection (C) /
detection
Health
Health
check (C) /
check
Thc / Phc_C
Thc / Phc_C
thc
-(thc + Thc)
Health
Health
check (C) /
Tcheck
P
hc /
Heat
steer i
Heat
(C) / Heat steer
steer
i (T)
Ths_C /
i / Ths_T /
Phs_C
Phs_T
Steer (C)
/ Ts_C /
Ps_C
Steer
-ths +
Ths_E
Steer (M)
/ Ts_M /
Ps_M
hc_C
Computation
Mechanical
Thermal
Heat
wheel j
(C) /
Thw_C /
Phw_C
Heat wheel
Heat
j wheel j
(T) / Thw_T
/ Phw_T
Drive (C)
/ Td_C /
Pd_C
-thw +
Thw_E
Drive
Drive (M)
/ Td_M /
Pd_M
Scheduling
Primary resource:
Computation
Auxiliary resource:
Thermal
Auxiliary resource:
Mechanical
Health
check (C) /
Thc / Phc_C
thc
Heat
steer i (T)
/ Ths_T /
Phs_T
Hazard
detection (C) /
Thc / Phc_C
-(thc + Thc)
Heat
steer i
(C) / Ths_E
/ Phs_E
-ths
Steer (C)
/ Ts_C /
Ps_C
-ths + Ths_E
Steer (M)
/ Ts_M /
Ps_M
-Ts_C + Ts_M
Heat
wheel j
(T) / Thw_T
/ Phw_T
-thw + Thw_E
Heat
wheel j
(C) /
Thw_E /
Phw_E
-thw
Drive (C)
/ Td_C /
Pd_C
Drive (M)
/ Td_M /
Pd_M
Scheduling Example – Mars Rover
Solar Pow er Chart on Mars
Power constraints
 Different
16
14
solar power supply over
time
 Different power consumption over
temperature/time
Solar Pow er

12
10
8
6
4
2
0
7
8
9
10
11
12
13
14
15
16
Time
Resource
Duration
Power
-40 degC
Solar power
Battery
-60 degC -80 degC
14.9
10 max
12
10 max
9
10 max
Heat one motor
5
5.1
6.2
7.5
Heat two motors
5
7.6
9.5
11.3
Drive
10
7.5
10.9
13.8
Steer
5
4.3
6.5
8.1
10
5.1
6.7
7.3
10
Constant
4.7
2.5
5.7
3.5
6.3
3.7
Hazard detection
Health check
CPU
17
Previous Solution by JPL
JPL Solution - High Solar Power (a)
Heat steer
25
Over-constrained,
conservative
 Serialize
every operation to satisfy
power constraint
 Longer execution time and underutilization of solar power
 No scheduling tool is used –
manual scheduling
Drive
15
Steer
Hazard detection
10
Health check
5
CPU
0
5
15 20
25
30
35
40
45 50
55
60
65
70 75
80
85
JPL Solution - High Solar Power (b)
Heat steer
25
Not power-aware
without considering
power sources and consumers
10
Time
Heat wheel
20
 Scheduling
Drive
Pow er

Heat wheel
20
Power

15
Steer
10
Hazard detection
5
CPU
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75
Time
System heart-beat - moving two steps
(a) Begin with health check (b) no health check
Solution 1: High Solar Power (14.9W)
Our Solution - High Solar Power (a)
25
Heat steer
Max solar power: 14.9W at
noon
 Improved
utilization of solar
power
 Automated scheduling – use
scheduling tools
Drive
15
Steer
Hazard detection
10
Health check
5
CPU
0
5
10
Aggressive – do as much as
possible
motors while doing other
operations
 Fastest moving speed – no
waiting on heating
15
20
25
30
35
40
45
50
55
60
Time
Our Solution - High Solar Power (b)
25
Heat steer
 heating
20
Power

Heat wheel
20
Power

Heat wheel
Drive
15
Steer
10
Hazard detection
5
CPU
0
5
10
15
20
25
30
35
40
45
50
Time
System heart-beat - moving two steps
(a) Begin with health check (b) no health check
Solution 2: Typical Solar Power (12W)
Our Solution - Typical Solar Power (a)
Moderate solar power output
– 12W
 Improved
utilization of solar
power
 Automated scheduling – use
scheduling tools
Heat steer
20
18
Heat wheel
16
Drive
14
Power

22
12
Steer
10
Hazard detection
8
Health check
6
CPU
4
2
0
Moderately aggressive –
avoid exceeding power limit
constraint –heating
motors while doing other
operations
 Faster moving speed – some
waiting time on heating
10
15
20
25
30
35
40
45
50
55
60
65
70
Time
Our Solution - Typical Solar Power (b)
 Relaxed
Power

5
Heat steer
22
20
18
16
14
12
10
8
6
4
2
0
Heat wheel
Drive
Steer
Hazard detection
CPU
5
10
15
20
25
30
35
40
45
50
55
60
Time
System heart-beat - moving two steps
(a) Begin with health check (b) no health check
Solution 3: Low Solar Power (9W)
Our Solution - Low Solar Power (a)
Minimum solar power output –
9W
 Restricted
constraint – serialize
operations
 Automated scheduling – use
scheduling tools
Heat steer
Heat wheel
16
Drive
Power

20
12
Steer
Hazard detection
8
Health check
4
CPU
0
Conservative – same as JPL
solution
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
Time
Our Solution - Low Solar Power (b)
Heat steer
20
 Slow
moving speed
 Full utilization of low solar power
Heat wheel
16
Drive
Power

5
12
Steer
8
Hazard detection
4
CPU
0
5
10
15 20
25 30
35
40 45
50 55
60
65 70
75
Time
System heart-beat - moving two steps
(a) Begin with health check (b) no health check
Comparison

Existing solution



Conservative – long execution time, low solar power utilization
Not power aware – same schedule for all cases
Not intend to use battery energy
Solar power output Battery energy
14.9
0
12
55
9
388

Solar energy
672.5
817
675
% of solar energy
60%
91%
100%
Time
75
75
75
Moving distance
2 steps - 14cm
2 steps - 14cm
2 steps - 14cm
Our solution



Adaptive – speedup when solar power supply is high
Power-aware – smart scheduling on different power supply/consumption
Use battery energy when necessary
Solar power output Battery energy
14.9
6
12
149
9
388
Solar energy
604
720
675
% of solar energy
81%
100%
100%
Time
50
60
75
Moving distance
2 steps - 14cm
2 steps - 14cm
2 steps - 14cm
Evaluation

What is the value of our tool?
 Shorter

Is this valuable?
 More


execution time
energy consumption from battery
Is this bad?
How to evaluate the tool?
 Application


Heart-beat is not the correct level to evaluate
Map schedules to applications
 Power



level evaluation
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
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
JPL
Time frame Solar Power
0-610
611-1220
1221Total
14.9
12
9
Travel
Distance
16
16
16
48
Time
610
610
610
1830
IMPACCT
Energy
Cost
0
440
3114
3554
Travel
Distance
24
20
4
48
Improve
ment
610
610
160
1380
Energy
Cost
72
1569.5
786
2427.5
24.6%
31.7%
Time
Application-level Evaluation

Power-awareness
 Execution
speed scales with
power condition adaptively
Adaptive Scheduling by IMPACCT
Pow er
16
Solar
Pow er
14
Speed
JPL
12

Smart schedule
 Maximize
best case
 Avoid worst case

Tradeoff: cost vs. efficiency
 Use
Speed
IMPAC
CT
10
8
6
10
Tim e
20
30
Power
16
energy wisely
Solar
Power
12

Application-specific
knowledge
 Working mode parameters of
components
Battery
Power JPL
8
 Application-level
Battery
Power IMPAC
CT
4
0
10
Time
20
30