From: AAAI Technical Report SS-92-01. Compilation copyright © 1992, AAAI (www.aaai.org). All rights reserved.
Scheduling
Lessons
Learned
from
Autonomous
Power System
the
MarkJ. Ringer
Sverdrup Technology Inc.
NASALewis Research Center Group
Cleveland, Ohio 44135
Ringer@mars.lerc.nasa.gov
Abstract
Space Station Freedom,a Lunar base, or Martian base
represents a critical portion of such a system. TheAPS
project explores intelligent hardwareand soft’ware
architectures for efficient systemoperationandscheduling
of an electrical powersystem[Ringer 1991].
The Autonomous
PowerSystem(APS)project
the NASALewis Research Center is designed to
demonstratethe applications of integrated intelligent
diagnosis, control and sebeduling techniques to space
powerdistribution systems.Theproject consists of three
elements: the Autonomous
PowerExpert System(APEX)
for Fault Diagnosis,Isolation, and Recovery(FDIR);the
AutonomousIntelligent Power Scheduler (AIPS)
efficiently assignactivities start timesandresources;and
powerhardware(Brassboard) to emulate a space-based
powersystem.
TheAIPSsebeduler has been tested within the
APSsystem. This sebeduleris able to efficiently assign
availablepowerto the requestingactivities andshare this
information with other software agents within tile APS
systemin order to implementthe generatedschedule. The
/tIPS sebeduleris also able to cooperativelyrecoverfrom
fault situations by resehedulingthe affected loads on the
Brassboard in conjunction with the APEX
FDIRsystem.
/kIPS served as a learning tool and an initial
scheduling testbed for the integration of FDIRand
automated scheduling systems. Manylessons were
learned from the /kIPS sebeduler and are nowbeing
integrated into a newscheduler called SCRAP
(Scheduler
for ContinuousResourceAllocation and Plamdng).This
paper will serve three purposes:an overviewof the AIPS
implementation,lessons learned fromthe AIPSscheduler,
anda brief section on howthese lessons are beingapplied
to the newSCRAP
scheduler.
1.1 The Need For (Automated) Scheduling
Onboarda complexspacecraft manyactivities
must be performed, each competingfor a multitude of
temporal positions and limited resources. Ascheduler
mustassign start times to eachactivity withoutviolating
any resource or temporal constraints. The resources
onboardsuch a spacecraft will be vastly oversubscribed,
havingmanytimes moreresource requests than available
resources. This makes it a paramount objective to
efficiently utilize the available resources in order to
completeas manyactivities as possible.
Current NASAspace-based systems rely on
ground-based human-intensive scheduling methods.
Humansprovide the main scheduling intelligence for
constructing schedules. These schedules are then
transmitted to the spacecraft to be executed. If the
scheduling expertise and computersare ground-based,
every anomalythat occurs onboardthe spacecraft that
incurs a schedule modification wouldeanse significant
time delays and efficiency losses. With the advent of
more complexspace-based systems such as the Space
Station Freedomand beyond,a moreefficient automated
schedulingparadigmis necessary[Britt 1988].
1.2 The APS Project Scheduling
1.
Introduction and Motivation
Goals
The goal of the APSproject is automated
scheduling for space systems with proof-of-concept
demonstrationson a powersystemtestbed. In this process
only the high level goals of the systemare stated by the
humanoperators, that is, whichactivities should be
performed. This information is taken and the scheduler
attains the goal of activities executed.Thesebedulermust
Future NASA
spacecraft and planetary surface
installations will require larger and moresophisticated
infrastructure systems and living enviromnents. Such
systemswill consist of dozensof resources andhtmdreds
of attached loads. The electrical powersystem on the
52
not only knowhowto generatethe schedule, but mustalso
knowhowto implementthe schedule, and howto recover
fromsystemor load induceddeviations in the schedule.
2.
AIPS Implementation
Since schedulingcannottake place in a vacuum,
the schedulermustbe able to interact with other agentsas
well as cope with many operational concerns. The
schedulermustbe able to generatean initial schedule,it
must have domain specific knowledge of how to
implementthe schedule, it must be able to reactively
modify the schedule in the ease that the assumed
informationof the state of the systemchanges,andit must
be able to do this withinmetric time constraints.
2.1
Whatis being scheduled
The APSBrassboard is a powersystem testbed
that contains a set of powersupplies, switchgear, and
loads that emulate a space-based power system. This
hardwareis controlled by a set of embedded
controllers
capableof configuringthe state of the Brassboard.These
controllers are then used to configurethe Brassboardto
supply powerto the loads designated by the scheduler.
Figure 1 shows the current configuration of the APS
Brassboard. RBrs and RPC’s are remote controlled
switchesandan L representsa load attachedto the system.
Theloads attachedto the Brassboardare resistive
load banks. In order to moreclosely emulate a spacebasedpowersystemeachload is given a set of attributes
resemblingthose of a space-basedsystem. Eachactivity
(load) has a time varyingprofile of powerdemand,
earliest
start timeandlatest completiontimeconstraints, priority,
and temporalplacementpreference.
2.2
Cooperation Between AIPS and APEX
AIPSis responsible for assi£ning the power
requestingactivities attached to the Brassboardtemporal
positions and resources without overalloeating the
available power. APEXis responsible for the
implementationof the schedule generated by AIPS. In
order to adequately modelthe interaction betweenAPEX
and AIPS, a set of protocols was developed to
communicatedifferent scheduling and rescheduling
procedures. Protocols were developed to generate an
initial schedule and modifyexecutingschedules. Figure
2 showsa graphicalrepresentationof a schedulegenerated
by AIPS. A chart showingthe interaction betweenthe
three portions of the APSproject is given in Figure3.
2.3
Scheduling Methods Used
Twomodesof schedule generationare neededfor
any integrated schedulingsystem.Theability to generate
an initial scheduleandthe ability to modify(resehedule)
an already executingschedulein the ease of an anomaly.
In the formercase, a metricamountof time is allocatedto
the schedulerafter whicha solution mustbe returned. In
the latter case, the reschedulingresults are usuallyneeded
as soonas possible.
n
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2: 0~
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betland
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4~ 00
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Figure 2 Representative Schedule Generated by AIPS
The AIPSscheduler has two modesof schedule
generation used for scheduling and rescheduling. The
schedulingengineis all incrementalschedulerthat uses a
set of activity selection andplacementheuristics [Sadeh
1989]. Theseheuristics are used to construct a schedule
by taking each activity one by one, anddeterminingwhere
to place the activity on the timeline.Theseheuristics also
formthe basis of the reschedulingengine.
Whenthe scheduler is given moretime, it will
use the samebasic heuristics along with a Monte-Carlo
type optimization methodto generate multiple schedules
Figure 1 Brassboard PowerSystemConfiguration
53
basedon the heuristics. Since the heuristics use local
goodnessinformation, they do not produceglobally good
schedules.Smallperturbationsto these heuristic decisions
will often improve the efficiency of the generated
schedule. Eachschedule is rated based on a goodness
rating and whentime to generate a schedulehas run out,
the best schedule (that has been saved in memory)
returned. Witha relatively hugestate space of solutions
this methodworksquite well probingmanyportions of the
state space that look promisingbasedon the heuristics.
between the relative value of activities. Timewas
partitioned at a granularity of five minutes. This wasa
reasonablesimplification since the time for APEX
and the
Brassboard to be configured was on the order of one
minute. The ability to schedule within metric time
constraints wasincorporated. A graphical interface was
available for both scheduledisplay and human-scheduler
interaction.
The largest assumption made about the
environment
wasthat all temporaldurations and resource
requestsare exact. In a real-life situation, if an activity
requests 100 watts for one hour the probability of the
activity using a constant100 watts or lasting exactlyone
hour is quite small. The problemsincurred mayinclude
undervoltage/overcurrent
conditionscausedby higher than
expected demandsas well as propagation of temporal
constraints amongactivities causedby an extensionof an
activity’s duration. Theneedfor sometype of temporalor
resource padding is necessary. This paddingdecreases
schedule efficiency although it mayimprove overall
implementedscheduleefficiency since the schedule will
not haveto be modifiedas often with the paddingadded.
, ..,
l ~hedu~mshfo
[
A~
xr~
~’~ ="
I /~tivity Start T~n~l
.........
/
~IFDI~R-les
~aeaumg
L~aowleagq~
[Sah~lule
!Schedule
Generation
---i"~AztivP"
itmI~ementation tttues
and
UserInterface
’~
State
Resource
Data Bras’~boa~d
Switch st ~kSwiteh
atesand
ControlI l
Messages
I lPower
~ llnformation
BRASSBOARD
Power ~qouro,m
Swit~hgear
ISca~sors
3.2 Perspective
[Loads
Figure 3 APSComponentFunctionality
Muchwas learned about scheduling, but even
morewas learned about implementinga schedule in an
automateddomain.Thewholeobject of scheduling is to
producethe best overall systemefficiency. In order to
increase the efficiency of the implemented
schedule, most
newideas point to the needfor the ability for real-time
reaction in the scheduler[Johnston1989]. Hereare three
examples,
Conventionalschedulers use temporalpaddingto
increase the probability of executing a schedule.
Temporaluncertainties cause the forwardpropagationof
predecessor/successorconstraintsandresource
availability.
If activities are padded,andthis paddingis not used,it is
wasted. It maybe possible, however, to assign this
temporalposition/resourceto anotheractivity. This would
entail moving
anotheractivity forwardin time to fill the
temporalposition/resource left unusedby the previous
activity. This demonstratesa need for reaction in the
scheduler.
Supposea 500 watt cooling fan operates only
whenthe experimenttemperature rises above a certain
threshold. This mayonly operate 10%of the time (10%
duty cycle). Howcan the resources be allocated to
preventoversubscriptions?If 500 watts are continuously
allocated 90%of this energywill be wasted(of course,
conflict free schedule is guaranteed). Energybalancing
betweenmultiple duty cycle activities can be used, but
problemsarise if all these activities turn on at the same
time. Reactionis neededto delay someof these events if
they desire to consumepowerwhenit is not immediately
available. In addition, there is the possibility of
Reschedulingmust also be accomplished"nonnervously",that is, withas little deviationto the original
schedule as possible [Biefeld 1990, Zweben1990]. In
systems with humaninteraction the original schedule
shouldbe followedas closely as possible in order to not
disturb the humansinteracting with the system. To
accomplishnon-nervousreseheduling, AIPSuses a set of
heuristics that judgethe amountof perturbationcausedby
a schedulemodificationversus the changeof goodnessof
the newschedule.
3.
Lessons Learned
Lessons were learned from the desiga~ of the
scheduler,implementing
a schedulerin a real system,and
integrating schedulingwith an FDIRsystem. Someof the
lessonslearnedrepresentedshortfalls in the original AIPS
scheduler while others represented ideas for the
improvement
of the overall efficiency of the scheduling
system.
3.1
Retrospective
Manyof the concepts implementedin the AIPS
scheduler workedquite well. Timewasbroken downinto
smaller schedulinghorizonsin order to makethe problem
solution feasible. Priorities were used to delineate
54
performingenergybalancingin the powersystemdomain.
Withenergybalancinghowever,it is necessaryto use a
storage type resourcesuchas a battery.
When using reaction,
think about the
"moveability"of an activity. In a SpaceStation domain,
a dishwashingactivity is muchmore moveablethan a
medical experimentusing two crew membersand various
ground-basedexperts. The dishwashingactivity has very
few attached dependencieswhile the medicalexperiment
wouldrequire the movement
of manyhumaninteraetors.
Thedishwashingactivity is easier to moveand a small
temporalposition changewill not affect it as long as the
dishes are washedbefore the next meal. This information
can be used to makereactive modificationsto the schedule
without impactingthe humanswhowill haveto interact
with the system.
Theideas of temporalpaddingusage, duty cycle
balancing, and activity moveabilitywill allow for more
efficient use of limited resources. Of course, a scheduler
(and testbed architecture) that allowsthese ideas to
implementedremains to be built and tested. The next
section will briefly describe this newscheduler.
4.
resource level tlmt providesan allowancefor deviations
from that level. This would point to the use of a
combinationof reaction and prediction. All schedulers
that operate in a real domainactually combinethe two,
but the idea of SCRAP
is to provide a frameworkthat
allowsthese ideas to be implemented
efficiently.
4.2 How to Combine Prediction
Even though building an initial schedule is
computationallyintense, the needto continuouslymodify
the schedule during execution is even more difficult
becauseof the tighter time constraints in the reseheduling
domain.Whenreseheduling, all temporal and resource
constraints propagateforwardcausingevenmoreconflicts
in the schedule, also knownas the ripple effect.
Propagatingtemporaland resource eoustraints during a
reschedule clobbers previously computedfuture portions
of the schedule. If reschedulingoccursoften, the entire
precomputed schedule may be reeomputed by the
reschedulingengine. This is an extremecase but proves
the point that it maynot be necessaryto construct the
initial schedulewith a great level of detail. Thereforeit
maybe wiseto schedulefar termactivities withless effort
or detail than near term activities. In the SCRAP
schedulerthis is accomplished
by using multiplelevels of
abstraction whenschedulingactivities. Further into the
future the scheduleis constructedabstractly, whilenearer
to the executiontime moreprecision is used. Also, more
in-depth schedulingmethodsare used for times nearer to
the executiontime than for timesfurther into the future.
Implementing the Lessons Learned
The SCRAPscheduler is currently under
development.Generalimprovements
in the representation
of the SCRAP
scheduler include multiple resources,
multiple resource types (capacity, consumable, and
storage), onesecondtime granularity, activities broken
into tasks, andmultiple levels of scheduleabstraction.
Since the previoussection showeda needfor reaction, a
schedulingparadigmthat makesreaction easier wouldbe
beneficial.
4.1
and Reaction
I
Prediction vs. Reaction
..........
: ......
Twogeneral categories can be delineated in
scheduling:predictive and reactive systems. Predictive
schedulingallows the efficient allocation of available
resources to activities by generatingschedulesbased on
predicted knowledge
of the activity andresource states.
This type of schedulingworkswell in static domainsbut
is often hard to implement
andless efficient in complex,
uncertain, anddynamicdomains.Reactionprovideseasier
implementation in dynamicdomains, but sacrifices
resourceusageefficiency causedby the lack of knowledge
used to generate schedules.
In most real world problemsa combinationof
static and dynamic domains exist. For exeanple, a
completelyreactive scheduler mighthave no information
on predicted resource demandsof an activity, while a
completely predictive scheduler would assume exact
temporal durations and resource requests. Usually, a
combinationof these methodsare used with a predicted
55
,~
I i.
I ~_Z’1--3------7--t ..... "t
~L---.-it’""
Figure 4 The SCRAP View of Scheduling
Multipleabstractions basedon temporaldistance
from the execution time will allow for moreefficient
forward temporal propagation of constraints in the
schedule since less information is used for the future
portions of the schedule. The future portions of the
abstractly generatedscheduleserve as apartially computed
schedule whenit comestime to actually schedule at a
moreprecise level. Theschedulingtimeline can be looked
at as a rolling horizon, with the future comingcloser to
the present as time ticks duringexecution.
Figure 4 graphically showsthe general idea of
SCRAP.The timeline movesin conjunction with the
movementof "real" time. Time "now"is the current
executiontime of the sebedule. Time"infinity" is some
time very far in the future. Thegantt chart showsI-beams
at different levels of schedulingabstraction. Thesolid
lines are precisely scheduled, the dashed lines are
scheduledat a medium
abstraction, whilethe dotted lines
are abstractly scheduled.Resourceoversubscriptionsare
allowedin the future since the schedulein those areas has
not beencomputedmoreabstractly. Nearerto the time of
execution, moreschedulingeffort and precision is used
and these resourceconflicts will be eliminated.
Manyof the reactive situations stated in the
lessons learned section can be moreeasily implemented
using the SCRAP
paradigm. In an automateddomainthe
scheduler has muchmore control over the executing
schedule.Thiscontrolalongwith the ability to efficiently
modifythe sebedule during executionwill allow for all
overall implemented
scheduleefficiency increase.
Intersociety EnergyConversionEngineeringConference,
1988.
5.
[Zweben1990] Zweben,M., Deale, M., and Eskey M.,
"Anytime Rescheduling", NASAAmes Artificial
Intelligence BranchTechnical Report, February1990.
Conclusion
The AutonomousPower System project at the
NASA
Lewis Research Center is an ongoing effort to
demonstratethe use of knowledge-baseddiagnosis and
scheduling software in advancedspace-basedelectrical
power systems. The APSproject has completed one
development iteration.
A scheduling system was
developedfor the APSproject and integrated with an
FDIRsystem and hardware.The original AIPSscheduler
was successful as a learning tool and a newimproved
scheduler is being developed. Manynew ideas for
increasing the implementedschedule efficiency will be
realized using the SCRAPparadigm. The SCRAP
schedulingparadigmwill allow for moreefficient use of
the availableresources.
Acknowledgements
This workswas performedunder NASA
contract
NAS3-25266
with Jim Kish as Technical Coordinator.
References
[Biefeld 1990] Biefeld, E., Cooper, L., "Operations
MissionPlanner: Final Report", JPLPublication 90-16,
March,1990.
[Britt 1988]
Britt, D.L., Gohring, J.R.,
Geoffrey, A.L., "TheImpactof the Utility PowerSystem
Concepton Spacecratt Activity Scheduling",Proe. 23rd
56
[Johnston 1989] Johnston, M., "Knowledge-Based
Telescope Scheduling", In Knowledge-Based
Systemsin
Astronomy,Springer-Verlag, 1989.
[Ringer 1991a] Ringer, M.J., Quinn, T.M., and Merolla,
T., "Autonomous
PowerSystem:Intelligent Diagnosisand
Control", Proceedings of the NASAGoddardConference
on SpaceApplicationsof Artificial Intelligence, 1991.
[Ringer 1991b] Ringer, M.J., "Autonomous Power
System: Integrated Scheduling", Proceedings Space
Operations, Applications, and Research Symposium,
NASA
JohnsonSpace Flight Center, Houston,Texas, July
1991.
[Sadeh 1989] Sadeh, N., and Fox, M.S., "Foens of
Attention in an Activity-BasedScheduler",In Proc. NASA
Conferenceon SpaceTeleroboties, Pasadena,California,
1989.