validating resources in least commitment scheduling nazma ferdous & mark giuliano space...
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Validating Resources in Least Commitment Scheduling
Nazma Ferdous&
Mark GiulianoSpace Telescope Science Institute
October 24, 2006
Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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HST Observation Scheduling
Two Phased Approach
• Uncertainty in HST orbit.
• Big problem size (~ 2400 observations per year).
• Better long term HST usage. Year-based
Long Range Planning (Assigns 8 week long
window for start time)
Week-basedShort Term Scheduling
(Assigns start time)
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1. Calculating Constraint Window
Observation constraint windows are calculated from all physical and other constraints, which denotes the timeline of when the observation can be scheduled.
2. Generating Plan Window (PW) Using least commitment scheduler SPIKE, observations are assigned plan
windows, which are the preferred window for scheduling.
3. Computing Resource Consumption
Long Range Planning (LRP)
Feb Mar Apr Jun Jul Aug Sep Nov0
1
Feb Mar Apr Jun Jul Aug Sep Nov0
1
Plan Window
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Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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SPIKE Resources• Total orbits per day (15 per day)
• South Atlantic Anomaly (SAA) related resources– SAA free orbits per day (6-7 per day)
– SAA impacted orbits per day (8-9 per day)
Sun
Target
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SPIKE Resource Model • Equally distributes the total resource required by an observation over
the range of the PW.
• Depicts general probability of resource contention/under usage.
O1
O2
O3
Day 1 2 3 4 5 6 7
Plan Windows
2Total Orbits
Consumed 0 2 3 2 2 0 0Available 2 0 -1 0 0 2 2
10
O1
O2
O3
Duration
2 34
State Day Over-subscribed 3 Leveled-subscribed 2,4,5 Under-subscribed 1,6,7
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Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model LimitationsResource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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Resource Model Limitations• Creates false positives.
• No oversubscribed days. • Yet, no feasible schedule exists !
O1
O2
O3
Day 1 2 3 4 5 6 7
Plan Windows
O4
Duration3 364
O1
O2
O3
O4
2Total Orbits
Consumed 0 4 4 4 4 0 0 Available 4 0 0 0 0 4 4
10
43
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Resource Model Limitation (cont.)
• Create false negatives.
• Day 3 and 4 are oversubscribed. • Yet, a schedule can be generated !
O1
O2
O3
Day 1 2 3 4 5 6 7
Plan Windows O1
O2
O3
Duration
3 22
1Total Orbits
Consumed 0 1 2.5 2.5 1 0 0 Available 2 1 -0.5 -0.5 1 2 2
0.50
21.5
2.5
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Initial Resource Profile
Data from 2006 observation cycleTotal Observations: 2389
Total Orbits Required: 3776 Total Orbits Oversubscription: 1029
Plan Window Generation ++
Short Term Scheduling
LRP
Plan WindowGeneration
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Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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Short Term Scheduling
Plan WindowGeneration
PlanValidation
Tool
LRP
Short Term Scheduling
LRP
Plan WindowGeneration
Plan Validation ToolGiven a Long Range Plan,
• Treat the plan windows for as the constraint windows.
• Assign 1 day-long scheduling window to the observations, essentially committing all resources required for the observation to that day.
• Analyze the resulting resource profile.
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Validation Tool Implementation
• Initial Guess- Scheduling windows are assigned w/o violating the
resource limit if possible. If not, window with least resource contention is chosen.
• Repair- A systematic repair (e.g. utilizes iterative deepening
concept) is used to flatten the resource profile, minimizing false alarm as much as possible.
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Iterative Deepening (ID) Repair
• Start at iteration 1 and progressively increase the iteration by 1.• At iteration i, for each conflicted observation, perform a depth-first
search, looking for chain of moves of length i, to resolve the conflict. • The algorithm stops when max iteration (user specified) is reached or
no resource contention exists.
Iteration Operation 1 Oversubscribed Undersubscribed 2 Oversubscribed Leveled subscribed Undersubscribed 3 Oversubscribed Leveled subscribed Leveled subscribed Undersubscribed n Oversubscribed Leveled subscribed Leveled subscribed … (n-2 moves) … Undersubscribed
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ID : An example Resource Limit: 2 observations are allowed per day
1 2 3 4 5 6 7 8 9 10 11
U L O L U O L U O L L
AF
BC
DE
O : Over Subscribed Days
L: Level Subscribed Days
U: Under Subscribed Days
Observations PW Conflicted?
A 3 - 5 *
B 5 - 7 *
C 7 - 9
D 9 - 11 *
E 4 - 10
F 1 - 4
A C
B D
FE
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1 2 3 4 5 6 7 8 9 10 11
U L O L U O L U O L L
ID : Iteration 1 Oversubscribed Undersubscribed• Observation Chain:
• Actions Performed:
1 2 3 4 5 6 7 8 9 10 11
U L L L L O L U O L L
AF
BC
DEA
Move A from day 3 to day 5
A C
B D
FE
ABD
√XX
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ID: Iteration 2Oversubscribed Leveled subscribed Undersubscribed• Observation Chain:
• Actions Performed:
1 2 3 4 5 6 7 8 9 10 11
U L L L L O U L O L L
B
1 2 3 4 5 6 7 8 9 10 11
U L L L L O L U O L L
FB
CD
EA
1 2 3 4 5 6 7 8 9 10 11
U L L L L L L L O L L
C
Move C from day 7 to day 8 to create room for B
Move B from day 6 to day 7.
A C
B D
FE
B,
D, E
A X
XC √
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1 2 3 4 5 6 7 8 9 10 11
L L L L L L L L O U L
D
1 2 3 4 5 6 7 8 9 10 11
L L L L L L L L L L L
1 2 3 4 5 6 7 8 9 10 11
L L L U L L L L O L L
1 2 3 4 5 6 7 8 9 10 11
U L L L L L L L O L L
BFD
EA CF E
ID : Iteration 3Oversubscribed Leveled subscribed Leveled subscribed Undersubscribed• Observation Chain:
• Action Performed:
Move F from day 4 to day 1 to create room for E
Move E from day 10 to day 4 to create room for D
Move D from day 9 to day 10.
A C
B D
FE
D, E, A XBCF
XX√
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Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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Validation Tool Resource Profile
For Total Orbits per day ResourceFalse positives identified: 5
False negatives identified : 90Total oversubscription : 495 (compared to 1029)
Extending Algorithms for
PW Generation • New initial guess and iterative deepening repair algorithms were
extended for plan window generation.
• Resulting long range plan is more resource balanced.
• Validation tool identified fewer false positive/negatives and oversubscription.
Short Term Scheduling
Plan WindowGeneration
PlanValidation
Tool
LRP
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New Resource Profile
Data from 2006 observation cycleTotal Observations: 2389
Total Orbits Required: 3776 Total Orbits Oversubscription: 558 (compared to 1029)
Validation Tool Resource Profile
For Total Orbits per day ResourceFalse positives identified: 2 (compared to 5)
False negatives identified : 179 (compared to 90)Total oversubscription : 142 (compared to 558)
Outline
• HST Observation Scheduling Process
• Resource Model
• Resource Model Limitations
• Plan Validation Tool
• Operational Results
• Summary & Future Directions
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Summary
• An automated resource verification system is implemented for the long range planning.
• Streamlined the long range planning process.• Significantly reduced human cost and saved
time.• Long range plan is more resource balanced.
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Future Directions
• While repairing resource conflicts, knowledge gained in one iteration can be used in subsequent iterations.
• Couple “PW Generation” and “Validation Tool” more tightly so that resources are validated as the plan windows are laid out.
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Thanks
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Limitations• Swapping observations is not allowed.
• Demonstrates diminishing return as the iteration increases.
• Using appropriate heuristics and utilizing domain knowledge is important.
O1
O2
O3
Day 1 2 3 4
Plan Windows O1
O2
O3
Duration 2 11
10
Total Orbits
32
Consumed 0 3 1 0 Available 2 -1 1 2
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