From: AAAI Technical Report SS-92-01. Compilation copyright © 1992, AAAI (www.aaai.org). All rights reserved.
Spike: AI Scheduling for Hubble Space Telescope
After 18 Monthsof Orbital Operations
MarkD. Johnston
SpaceTelescopeScienceInstitute
3700San MartinDrive,
Baltimore, MD21218 USA
johnston@stsci.edu
Abstract
This paperis a progressreport on the Spikescheduling system, developedby the SpaceTelescopeScience Institute for long-termschedulingof Hubble
SpaceTelescopeobservations.Spikeis an activitybased scheduler whichexploits AI techniques for
constraint representationandfor schedulingsearch.
Thesystemhas beenin operationaluse since shortly
after HSTlaunch in April 1990. Spike has been
adoptedfor several other satellite schedulingproblems:of particular interest has beenthe demonstration that the Spikeframework
is sufficientlyflexible
to handleboth long-termand short-termscheduling,
on timescalesof years downto minutesor less. We
describe the recent progress madein scheduling
search techniques, the lessons learned fromearly
HSToperations, and the application of Spike to
other problemdomains.Wealso describe plans for
the future evolutionof the system.
with the systemsince the start of HSTflight operations.This
is followedby a descriptionof the changesrequired to adapt
Spike to other satellite schedulingproblems.Weconclude
with somecommentson the implementationof Spike, and
on our plans for future work.
2 Overviewof HSTScheduling
HSTscheduling is a large problem:some10,000 to 30,000
observationsper year mustbe scheduled,each subject to a
large number
of operational andscientific constraints. Most
of the operationalconstraintsarise fromthe lowearth orbital
environment
of the telescope. Withan orbital periodof about
96minutes,potential targets are onlyvisible for a portionof
eachorbit before they are occultedby the earth. Thereare
constraintsdueto guidestar availability, avoidingthe earth’s
radiation belts, andstray light fromthe sun, moon,or bright
earth. Thereare also constraints arising fromthermal and
powerconsiderations, whichtend to restrict the allowable
attitudeof the satellite at differenttimesduringthe year.Sci1 Introduction
entific constraints are specified by astronomerswhenthey
Efficient utilization of expensivespace-basedobservatories define the exposuresto accomplishtheir scientific goals.
is an importantgoal for NASA
and the astronomicalcommu- Thesefrequently take the form of minimum
exposuretimes,
nity: the cost of facilities like HubbleSpaceTelescope temporal relationships amongexposures (before, after,
(HST)is enormous,and the available observing time
groupedwithin sometime span, separated by someminimuchless than the demandfrom astronomers around the
mumand/or maximum
interval, etc.). Astronomersmayalso
world. The Spike scheduling system was developedby the
constrain the state of the telescope in other ways,e.g. by
SpaceTelescopeScienceInstitute starting in 1987to help
requiring exposures whenHSTis in earth shadow(to
with this problem.Theaim of Spikeis to allocate observa- excludescatteredearthlight), by specifyingthe orientationof
tions to timescalesof daysto a week,observingall schedul- the telescope,or by configuringoneof the six instrumentsin
ing constraints, and maximizing
preferencesthat help ensure a particular mode.A recent changeto the HSTgroundsysthat observationsare madeat optimal times. Spikehas been tems nowpermits the scheduling of two instruments for
in use operationallyfor HSTsince shortly after the observa- simultaneousoperation: this is expectedto significantly
tory waslaunchedin April 1990.
increasethe amountof useful data taken by the telescope.
Althoughdevelopedspecifically for HSTscheduling,
Becauseof the design of the telescope and groundsysSpike wascarefully designedto provide a general frame- tem, nearly all HSTactivities mustbe scheduledin detail in
workfor similar (activity-based) scheduling problems.
advance. The detailed schedule specifies what commands
particular, the tasks to be scheduledare definedin the system will be executedby the onboardcomputers,and whencomin general terms, and no assumptionsabout the scheduling municationscontacts will be available for uplinking comtimescale were built in. The mechanismsfor describing,
mandsand downlinking data. Real-time interaction by
combining,and propagatingtemporaland other constraints
observersis limited essentially to smallpointingcorrections
andpreferencesweredesignedto be general. Thesuccessof
to place targets accuratelyinto the properinstrumentaperthis approachhas been demonstratedby the application of
ture.
Spike to the schedulingof other satellite observatories:
SchedulingHSThas been divided into two processes:
changesto the systemare required only in the specific con- the first is long-termscheduling,whichallocates observastraints that apply,andnot in the framework
itself.
tions to week-longtime segmentsover a scheduling period
In the followingwefirst providea brief descriptionof
of a yearor morein duration.Thisis the responsibilityof the
the HSTscheduling problemand of the Spike scheduling Spikesystem.Individual weeksare then scheduledin detail
framework.Wethen discuss someof the experience gained by the Science Planning and Scheduling System (SPSS),
whichorders observations within the weekand generates a
tion, i.e. the numberof constraint violations on scheduled
detailed command
sequence for the HSTcontrol center at
activities or on potential scheduletimes. Min-conflictstime
NASA
GoddardSpaceFlight Center. Further details on HST selectionis onesuchrepair heuristic, in whichactivities are
schedulingmaybe foundin [1,2].
movedto times whenthe numberof conflicts is minimized.
Both theoretical analysis and numericalexperimentshave
shownthat min-conflictscan be very effective in repairing
3 Spike and HST Long-Term Scheduling
goodinitial guesses[9]. Wehavefoundthat further improveHSTobserving programsare received at STScIin machine- mentcomesfromthe use of a max-conflictsactivity selecreadableformover national and international computernettion heuristic, whichselects activities for repair whichhave
works. Theyare then translated by an expert systemcalled
the largest numberof conflicts on their current assigned
Transformation
[3] into a formsuitable for scheduling.The time. Spikepermitsdifferent constraints to havedifferent
Transformationsystemcollects exposuresinto "scheduling conflict weights,whichcan be usedto causethe repair of the
units" whichare collections of exposuresto be executedcon- mostimportantconstraints first; in practice, however,all
tiguously. Transformationmakesuse of the Spike temporal constraints have so far been given the sameweight. Both
constraint mechanism
to collect and propagatetemporalcon- hillclimbing and backtrackingrepair procedureshave been
straints: these are madepath-consistent andsavedin files
tried, but hillclimbinghas beenshownto be the mostcostalong with the schedulingunit definitions. Spiketakes the
effective on problemsattemptedto date.
saved scheduling units and derives schedulingconstraints
The choice of a goodinitial guess is important for
andpreferencesfor them,basedon operationalandscientific
repair-based methods,and to this end wehave conducted
factors suchas those describedabove.Spikethen determines experimentson different combinationsof variable and value
an allocationof schedulingunits to weekswhichsatisfies all
selection heuristics to find the "best" combination.Overa
hard constraints and as manysoft constraints as possible.
thousandcombinationsof heuristics weretried by making
Constraints from different sources are combinedusing a
multiple runs on samplescheduling problems.Theadopted
weight-of-evidencemechanism
generalized to cover a con- initial guessheuristic selects most-constrained
activities to
tinuous time domain,as describedin detail elsewhere[4].
assign first, wherethe numberof min-conflictstimesis used
Theresult is a set of "suitability functions"whichindicates as the measureof degreeof constraint. Min-conflicttimes
goodnessover time for each schedulingunit, andalso indiare assigned, with ties broken by maximum
preference as
cates times whena schedulingunit cannotbe scheduleddue derivedfromsuitability functions.
to violations of strict constraints. Mostof the HST-specific
Spike currently uses a rather simple technique to
schedulingdetails go into the definition of the suitability
removeconflicting activities froman oversubscribedschedfunctions, which,for long-termscheduling,are definedat a
ule: activities to be removed
are selectedbasedon lowerprihigh level of abstractionandrelatively coarsetime granular- ority, higher numbers
of conflicts, andlowerpreferencetime
ity. Moredetails about Spikeconstraint representationand assignments.
If there remaingapswhenall conflictingactivimanipulationmaybe foundin [5].
ties havebeendeleted, then a simplebest-first pass through
Spiketreats scheduleconstructionas a constrainedoptithe unscheduled
activities is used to fill them.This final
mizationproblemanduses a heuristic repair-basedschedul- phaseof "scheduledeconflicting"has beenlittle studiedand
ing search technique. An initial guess schedule is
is an area whichcouldbenefit fromfurther effort.
constructed, whichmayhave temporalor other constraint
Spikeprovidessupportfor reschedulingin several ways.
violations as well as resourceoverloads(in fact, giventhat
Twoworth mentioningin particular are task locking and
HSTobservingtime is intentionally oversubscribedby about conflict-causeanalysis. Tasksor sets of tasks can be locked
30%,it is knownahead of time that there is no feasible
in place on the schedule,andwill thereafter not be considschedule that can accommodate
all the requested observa- ered during search or repair (unless of course the user
tions). Repairheuristics are applied to the initial guess
unlocks them). Thesetasks represent fixed points on the
schedule until a preestablished level of effort has been schedule.Conflict-causeanalysis permitsthe user to force a
expended.At that point observationsare removedto elimi- ’ task onto the schedule,thendisplaywhatconstraintsare vionate remainingconstraint violations, until a feasible sched- lated andby whichother tasks. Theconflicting tasks can be
ule remains. There are several important measures of
unassignedif desired, either individuallyor as a group,and
schedule quality employed,including the numberof obserreturned to the pool of unscheduledtasks. This helps with
vations on the schedule,the total observingtime scheduled, the mostcommon
reschedulingcase, wherea specific activand the summeddegree of preference of the scheduled
ity must be placed on the schedule, thereby disrupting at
observations.Theheuristic repair methodis fast, and typileast sometasks whichare already scheduled. A limited
cally manyruns are madeand the best scheduleis adoptedas
study of minimal-changerescheduling has been conducted
a baseline. TheSpike algorithm has desirable "anytime" [10], but muchmoreworkremainsto be donein this area.
characteristics:at anypoint in the processing
after the initial
Hillclimbingrepair methodslike the one used in Spike
guess has beenconstructed, a feasible schedulecan be prohave muchin common
with simulated annealing techniques
duced simply by removingany remaining activities with
such as described by Zwebenet al.[ll]. Oneof the open
constraintviolations, as describedfurther below.
research issues is whichtechnique has an advantage on
Therepair heuristics usedby Spikeare basedon a very whichtypes of problems.
successful neural networkarchitecture developedfor Spike
[6,7] andlater refined into a simplesymbolicform[8] which
has madethe neural networkobsolete. TheSpikerepair heuristics makehighly effective use of conflict count informa2
4 The Experience
of HST Operations
Shortly after HSTwaslaunchedit was discoveredthat the
telescopemainmirrorhad beenfiguredincorrectly, resulting
in lowerresolutionthan anticipated. This has not only liraRedthe scientific usefulnessof HST(althoughit still remains
far superior to any ground-basedtelescope), it has also
greatly disrupted the scheduling process. Observingplans
madeyears in advanceof launch have had to be revised,
leading to a shortage of ready-to-scheduleobservingprogramsand thus reducingthe efficiency with whichschedules
could be generated.This problemstill affects ongoingoperations, andas a result Spikehas only oncebeenusedto generate a true long-term schedule. Instead, Spike is used
routinely to identify observationsto place in the schedule
approximately
two monthsinto the future. Asthe characteristics of the telescope and instrumentshavebecomebetter
understood, the pool of observing programshas been growing: the secondround of openproposal selection will be
completedin December
1991, and weanticipate that by the
Springof 1992a sufficient pool will exist to permitlongrange planning as originally expected. NASA
is nowplanning a servicing missionto correct the HSToptics in early
1994.
Themostsignificant lesson learned since launch, however, is the impactof high levels of changeon the planning
andschedulingsystems. Instead of the anticipated level of
about 10%of proposalschanging,the actual rate of change
has been closer to 100%.While someof this change is
clearly attributable to the discoveryof HST’ssphericalaberration, manyother factors havecontributedas well: nearly
every instrument on the telescope has demonstratedunexpectedbehaviorin one formor another, and each has led to
revisionsin observingplans to compensate.
Thenet effect is
that changeis the norm,not the exception,to the extent that
stress has beenhigh on the softwaresystemsandon the people whooperate them.Theproblemstemsfrom the fact that
an observing program mayconsist of manyhundreds of
exposures,whichcan all be at different stages of the scheduling pipeline. If an observingprogramis changed,users
mustbackup to the beginningof the processfor that program,thus workdoneon the previousversion is potentially
wasted.Alternatively, a newobservingprogramcan be created to describethe changedportionsof the original one, but
then keepingtrack of active and obsolete portions of the
originalis required.
If there is any recommendation
to be madeto developers of future systemslike those for HST,it is to build in the
expectationof changefromthe outset [12]. Eventhoughthe
initial costwill be higher,the operationalcostswill be significantly lower.
Spike and the other HSTground systems have been
exercisedseveral times on "targets of opportunity"--- programsto be scheduledand executedon an crash basis. Turnaroundhas beenas short as a fewdays, whichis well within
the pre-launchexpectations.Onesuch target of opportunity
programtook the pictures of the dramaticstormon Saturnin
December1990, whichwere subsequentlymadeinto a timelapse movie.
5 Hierarchical and Short-Term Scheduling
Spike has been adoptedfor schedulingthree future astronomicalsatellite missions:
¯ the ExtremeUltraviolet Explorer(EUVE),an ultraviolet
telescope built and operated by UCBerkeleyand Goddard SpaceFlight Center,
¯ ASTRO-D,
a joint US-JapanX-ray telescope, and
¯ XTE,the X-ray timing Explorer (MIT/GSFC)
to study
time-variabilityof X-raysources.
Theadaptation of Spike for these problemshas led to
the successfuldemonstration
of the flexibility of the Spike
scheduling framework. As indicated above, Spike was
designedso that newtasks and constraints can be defined
without changing the basic framework.For ASTRO-D
and
XTE,Spikeis operatedin a hierarchical manner,with longterm schedulingfirst allocating observationsto weeksmuch
as they are for the HSTproblem(and with similar types of
long-termconstraints and preferences). Theneach weekis
scheduledin detail, subject to the detailed minute-by-minute
constraints of low earth orbit operation. Themajorchanges
required to implementshort-termschedulingwere:
¯ a new type of task that can have variable duration
dependingon whenit is scheduled, and whichcan be
interrupted and resumedwhentargets are occultedby the
earthor the satellite is in the radiationbelts
¯ newclasses of short-term scheduling constraints which
moreprecisely modeltarget occultation, star tracker
occultation, groundstation passes, entry into high radiation regions, maneuverand setup times betweentargets,
etc.
¯ an interface betweendifferent hierarchical levels, by
whicha long-termscheduleconstrains times for shortterm schedulingand conversely
¯ a post-processor whichexaminesshort-term schedules
for opportunitiesto extendtask durationsandthus utilize
anyremainingsmallgapsin the scheduleto increaseefficiency
All of the general constraint combinationand propagation mechanisms,
and the schedulesearch techniques, apply
directly to both long-termandshort-termscheduling.Figure
1 illustrates the applicationof Spiketo short-termscheduling
for a sampleof X-raytargets such as mightbe observedby
ASTRO-D
or XTE.Note that several observations are brokento fit aroundoccultations andso are taken in multiple
segments.
Mostof the effort required to apply Spiketo the new
problemswaslimited to the specific domainmodellingneeessary, whichtypically involvescomputationrelated to the
geometry
of the satellite, sun, target, andearth. Theseproblemscan be expectedto differ fromonesatellite to another,
andit is not surprising that different modelsare required.
Someof the modellingincludes state constraints, although
Spikedoesnot performexplicit planning(see, e.g. [13]).
EUVE
is unusualin that it makeslong (2-3 day) observations, in contrast to HST,XTE,and ASTRO-D
whichtypically makenumerousshort (15-40 minute) observations.
a consequence,EUVE
is schedulableover year-long intervals withoutbreakingthe scheduleinto hierarchical levels.
Oneof the moreinteresting results from a comparisonof
search algorithms for scheduling EUVE
wasthat the Spike
repair-based methodsgained an extra 20 days of observing
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92,0021/2
Figure1: An exampleof Spikeoutput on short-termschedulingof astronomicalobservations.Shown
is a 24-hourportion of a 7-dayschedule. Thestart-time suitability for eachexposureis plotted as the uppergraph, withinterruptionsdueto target blockageby the earth andby
satellite passagethroughhigh-radiationregions. Theavailableexposureintervals are shownbelowas openbars, whichare filled in to indicate the actual scheduledtimes. Someof the observationscanbe fit withinone orbit; others mustbe interruptedandthus spanseveral orbits.
time in a year, whencomparedto the best incremental scheduling approach.
6
Spike Implementation
The implementation of Spike started in early 1987 and was
initially based on Texas Instruments Explorers as the hardware and software environment. The Spike graphical user
interface
was implemented in KEE CommonWindows
(Intellicorps, Inc.), but the remainder of the system (about
40,000 lines of code) used only Common
Lisp and the Flavors object system. At HSTlaunch, STScI had a complement
of 8 TI Explorers and microExplorers used for Spike operation, developmentand testing.
Since the initial developmentof Spike began there has
been a great deal of evolution in Lisp hardwareand software.
A significant amountof effort has gone into modifying the
system to keep current with these changes. In late 1991 we
are in the process of movingfrom Explorers to Sun SparcStation IIs as the primary operations and developmentworkstation. All of the Flavors code has been automatically
4
converted to the CommonLisp Object System. The Lisp
used on the SparcStation is Allegro CommonLisp from
Franz Inc. Allegro CLsupports a version of CommonWindowsbased on X-windows,and so the user interface continues operate on Unix platforms as it did on the Explorers. We
are presently investigating the use of alternative windowsystems, and have prototyped the use of CLX,CLIM,and Motif
for the user interface (the latter is based on the publicly
available GINA/CLM).
Weexpect to see a complete redesign of the user interface in the next year. Spikecan also generate high-resolution Postscript versions of schedules and
constraints; one exampleof this is shownin Figure 1.
Updating Spike for new Lisp language features has not
been difficult. There are, however,plans to removesomefeatures that were developed for Spike which have since
become part of the language (such as a logical filename
mechanism).At present there are no plans to convert any of
the system to C or C++.
7
Future Directions
Several significant enhancementsto Spike are planned
over the next year. Oneof these, a rewrite of the graphical
user interface, has already been mentioned above. Another
enhancementdeals with tracking the status of HSTobserving programs and exposures. All scheduled programs pass
from the proposal entry system through Spike, while feedback on scheduling and execution status is received by Spike
both from SPSSand from the HSTdata analysis pipeline.
This provides information to Spike users which forms the
basis for rescheduling decisions. Weplan to integrate this
data into a relational database, along with additional information from the HSToptical disk data archive, which will
provide a central source of information on the status of all
HSTobservations.
Weare also planning several systematic studies of the
Spike scheduling search heuristics to see what further
improvementscan be made, either in performanceor in quality of schedule. Thesewill include the initial guess, repair,
and deconflict strategies. Wealso plan to investigate whether
the use of short-term scheduling on the HSTobservations
can improve the quality of the long-term schedule sent to
SPSS. There are, however, no plans to have Spike do the
final short-term scheduling for HST,due to the extreme cost
of integration with the existing telescope and instrument
commanding software which generates
the command
sequencesfor the spacecraft.
8 Conclusions
The Spike system has performed as planned in the first 18
months of HSToperations. The success of Spike helps demonstrate the utility of AI technology in NASA
flight operations projects.
The flexibility
of Spike has been
demonstratedby adapting it for several other missions, and
by integrating long-term and short-term scheduling at different hierarchical levels of abstraction in the sameconstraint
representation and scheduling search framework.
Acknowledgments:The author wishes to thank Dr. Glenn
Miller and the present and former members of the Spike
development team (Jeff Sponsler, Shon Vick, Tony Krueger,
Dr. MarkGiuliano, and Dr. Michael Lucks) for their efforts
which have led to the successful deployment of Spike. The
patience and support of the Spike user group, led by Dr.
Larry Petro, chief of the STScIScience Planning Branch, has
been muchappreciated. Stimulating discussions with Dr.
Steve Minton, Monte Zweben, Don Rosenthal, and HansMartin Adorf are gratefully
acknowledged. The EUVE
project at UCBerkeley helped with the initial Unix port, and
the ASTRO-D
and XTEstaff at MIThave continued to help
push the system in new and fruitful directions. The Space
TelescopeScience Institute is operated by the Association of
Universities for Research in Astronomyfor NASA.
References
[1] Miller, G., Rosenthal, D., Cohen,W., and Johnston, M.D.
1987: "Expert System Tools for HubbleSpace Telescope
Observation Scheduling," in Prec. 1987 GoddardConf. on
SpaceApplicationsof Artificial Intelligence; reprinted in
TelematicsandInformatics4, p. 301(1987).
[2] Miller, G., Johnston,M.D.,Vick,S., Sponsler,J., andLindenmayer, K. 1988: "KnowledgeBasedTools for HubbleSpace
TelescopePlanningand Scheduling:Constraints and Strategies", in Prec. 1988Goddard
Conf. on SpaceApplicationsof
Artificial Intelligence;reprintedin TelematicsandInformatics
5, p. 197(1988)
[3] Gerb,A. 1991: ’~l’ransformationReborn:A NewGeneration
Expert Systemfor Planning HSTOperations", in Prec. 1991
GoddardConf. on SpaceApplications of Artificial Intelligence, ed. J.L. Rash, NASA
Conf. Publ. 3110(Greenbelt:
NASA),
pp. 45-58; to be reprinted in the Dee1991issue of
TelematicsandInformatics.
[4] Johnston, M.D. 1989: "Reasoning with Scheduling Constraints andPreferences,"SpikeTeeh.Report89-2, Jan. 1989.
[5] Johnston, M.D.1990: "SPIKE:AI Scheduling for NASA’s
HubbleSpaceTelescope",M.D.Johnston,in Prec. Sixth IEEE
Conf.on Artificial Intelligence Applications(Santa Barbara,
March5-9, 1990), (Los Alamitos, CA:IEEEComputerSociety Press), pp. 184-190.
[6] Adorf, H.-M.,and Johnston, M.D.1990:"A Discrete Stochastic ’Neural Network’Algorithmfor Constraint Satisfaction
Problems",H.-M.Adorfand M.D.Johnston,in Prec. Int. Joint
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