energy-aware dynamic data-driven application … •motivation and overview ... •other program...
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Energy-AwareDynamicData-DrivenApplicationSystemsforTransportationMonitoringandAnalysis
RichardFujimoto,Aradhya Biswas,MarkJackson,SaBra NealComputationalScience&EngineeringMichaelHunter,Angshuman GuinCivil&EnvironmentalEngineering
GeorgiaInstituteofTechnology
Outline
• MotivationandOverview• GreenRuntimeInfrastructure• EnergyEfficientSynchronizationofDistributedSimulations
• EnergyEfficientDataDistributionManagement
• OtherProgramElements
DDDASSystemOverview
• Roadside,in-vehiclesensorsproviderealtimedata• Predictionusingonlinedata-drivendistributedsimulationandanalytics• Adaptation:travelerguidance,transportationsystemmanagement• Applications
– Reducetravelerdelays,fuelconsumption,emissions– DoDapplicationtoadaptivesurveillancesystems
Roadside Server
In-Vehicle Simulations
SimulationsonHandheld
Devices
Sensors
Mobile platforms
DoDApplication:VehicleTrackingEachmobiledeviceincludes• Sensors• Predictivedatadriven
simulationandanalytics• Wirelesscommunication
Autonomousteamofmobilesensorsmonitoringvehiclesinanurbanenvironment
Sense:determinecurrentlocationoftarget vehicles
Adapt:repositionsensorsinlightofpredictedlocations
Predict:projectlikelyfuturelocationsandpotentialdestinations
DDDASProcessingLoop
Otherpotentialapplicationincludetrackingspreadofforestfires,cloudplumes
Micro-ClusterHardwareJetsonTK1
CPU ARMA15(32-bit, 2.3GHz,4cores+1
lowpowercore)
GPU 192coreKepler,326GF/s(peak)
Memory 2GBLPDDR3
Storage 16GBeMMC
Networking Ethernet
FormFactor Devboard
I/O USB,HDMI,Serial
Micro-ClusterServerHardware• Jetson TK1Boards(TX1underevaluation)• Tunablecore(10x),memory(3x)
frequencies• PowerMon2measurementsystemClientHardware• Quadcore QualcommMSM8974
SnapdragonProcessor(LGNexus5cellularphone)
Communications:802.11n
TransportationSimulationCalibration
CalibrationProcedure
Uncalibratedsimulation(default
parameters)
NGSim DataSet:PeachtreeStreet,AtlantaGA
MonteCarloruns:identifyparameters
Flowratetests
Statisticaltests
TransportationSystemModeling
• Models– CellularAutomata– QueueingNetwork
• SectioninmidtownAtlanta
• NGSim dataset0
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0 1/20 1/10 3/20 1/5 1/4 E
nerg
y C
onsu
mpt
ion
(J)
Interarrival Rate (vehicle/sec)
Embedded SimulatorsEnergy Consumption
DES-Q Model CA Model
Historic Current(e.g.,last15minutes)
+ =(ω) (1-ω)
Bayesiansolution
Speedandaccelerationonalternateroutes
EnergyUseinData-driventrafficsimulation
BayesianPrediction
Outline
• MotivationandOverview• GreenRuntimeInfrastructure• EnergyEfficientSynchronizationofDistributedSimulations
• EnergyEfficientDataDistributionManagement
• OtherProgramElements
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Network(Internet/Ad-
hoc)FastCGI
(mod_fcgi)
Server
GlobalSync
Module
PushMessageModule
MessageAggregatorModule
CppCMScontroller
• Green Runtime Infrastructure (G-RTI) middlewareØ Distributed simulation integration framework based on DoD High Level Architecture
standard (IEEE 1516) supporting DDDAS simulation, emulation, and deploymentØ Scalable designØ Flexible, supporting wide variety of devices, Internet of ThingsØ Energy-efficient implementation of key services
Ø Time Management (Synchronization)Ø Data Distribution Management (Communications)
Green Runtime Infrastructure Middleware
Example 1: DDDAS Deployment• Real-time sensor,
crowd-sourced data• Traveler assistance:
mobile apps, in-vehicle systems with predictive simulations, analytics customized for individual travelers;
• Back end systems (simulations, analytics, databases) for system-wide analysis
• Communications over wireless networks, Internet
• Runtime Infrastructure (RTI) provides services for interconnecting sensors, mobile devices, and back end computing facilities, databases
• Green RTI (G-RTI): RTI optimized to minimize energy consumption in mobile devices and other computing elements
Example2:DDDASEmulation
• Development of DDDAS systems in the laboratory• Mix of actual software/devices (e.g., mobile apps on cell phones,
databases) and simulated elements (e.g., networks, vehicles, sensors)
• Real-time interaction among system components
Example 3: DDDAS Simulation
• Early stage DDDAS development• Simulated sensors, back end system, mobile applications, vehicles,
networks• As-Fast-As-Possible execution (may not be real-time)
DistributedSimulationEnergyProfiles
• LGNexushardware• CMBsynchronization
algorithm• Queueingnetwork
benchmark• Varymeasurement
parameter(replications)
• Profilingtechniquesdevelopedtoseparateenergyconsumedbydifferentlayersandcomponents
• Energyconsumedbyeachlayercanbesignificant
Outline
• MotivationandOverview• GreenRuntimeInfrastructure• EnergyEfficientSynchronizationofDistributedSimulations
• EnergyEfficientDataDistributionManagement
• OtherProgramElements
TimeManagementServices
• Servicesusedtosynchronizedistributedsimulations
• Timemanagementservicesmustsupport– Differentlocaltimeadvancemechanisms(timestepped,eventdriven)
– Synchronizationapproaches• conservative(asynchronous,synchronous)• optimistic(rollback)
• Goal:developenergyefficientsynchronizationalgorithms
PowerConsumption
Trace
EnergyCostofDistributedSimulation
• QueueingNetworkSimulation• Weakscalingexperiment,powerononedevice• Energyrequiredfordistributedsimulationsignificant
• LGNexushardware,AndroidOS
• Chandy/Misra/Bryantasynchronousnullmessagealgorithm
• YAWNSsynchronousalgorithm
Synchronization:Energy
• Syntheticworkload(onlysynchronizationoperations)• Lookahead parameter• Chandy/Misra/Bryant(asynchronous)
– NULLmessagesusedtosynchronizesimulation– Overheadincreasesasfunctionoflookahead
• YAWNS(synchronous)– Barriersusedtosynchronizecomputation– Moreefficienttimeadvanceatlowlookahead
Lookahead
Energy(J)
Lookahead
Messages
PowerandPerformanceTradeoffs• Powercostutilizingadditionalcores• NS3parallelsimulation• Microcluster platform
– 4coresperboard– Lowpowercore(“0core”datapoint)
• Performancew/powercap• Strongscalingexperiment• Powerawarespeedup:speedup
relativetolowpowercore(lowestpowerapproach)
• Novalueinusing5 or6coresfrompowerperspective
OptimisticEventProcessing(TimeWarp)
• Conservativemethodsincurenergyoverheadstopreventcomputationsfromadvancingtoofaraheadwhichmightresultinsynchronizationerrors– CMB:nullmessages– YAWNS:globalsynchronizations,reductioncomputation
• Optimisticprocessing(e.g.,TimeWarp)avoidssuchoverheads,butincursotherenergycostsnotpresentinconservativeexecution– Computationsthatarelatercanceledbysubsequentrollbacks– Statesavingoverhead– Therollbackcomputationsthemselves(includingsecondaryrollbacks)– ComputingGlobalVirtualTimeandreclaimingmemory
• Weconjecturethatoptimisticapproachesofferthepotentialforlowerenergyoverheadsprovidedrollbackoverheadscanbemanaged
EnergyReductionUsingOptimisticExecution
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0 2 4 0.6 0.8 1
Energy(J)
FractionofRemoteCommunications
EnergyUsingOptimisticExecution
YAWNS TimeWarp
• Syntheticbenchmark(PHOLD)• Onenodeofmicrocluster (4processors)• Efficientexecution(96-96%ofprocessedeventscommitted)• Suggestssimulationsmaybeabletouseoptimisticexecutionto
achievegreaterenergyefficiencies
Outline
• MotivationandOverview• GreenRuntimeInfrastructure• EnergyEfficientSynchronizationofDistributedSimulations
• EnergyEfficientDataDistributionManagement
• OtherProgramElements
HLADataDistributionManagementServices
• Publish/subscribecommunicationservices• Routingspace• Publicationregions(areaswhereinformationknown)• Subscriptionregions(areasofinterest)• Regionoverlapsdeterminewhoreceivesmessages• Regionsaredynamic
PublicationRegions
(e.g.,sensors)
SubscriptionRegion(e.g.,vehicles)
RoutingSpace
Googlemaps
B
A
C
DDMImplementationApproaches
Region-Based• Multicastgroupper
publicationregion– Messagessenttosinglegroup– Joingroupsbasedon
subscriptionregionoverlaps• Matchingcomputation
Grid-Based• Multicastgrouppergridcell
– Sendtogroupsforcellsoverlappingpublicationregion
– Joingroupsoverlappingsubscriptionregion
• Mayresultinirrelevantandduplicatemessages
Variationshavebeenproposed,e.g.,hybridapproaches
EnergyConsumption:Communications
• Data-driventrafficsimulation(midtownAtlanta)
• Streamoffixedsized(12byte)messages;varyfrequency
• Sendingconsumes~5xenergycomparedtoreceiving
• Aggregatemessages(12byteseach)atsender
• Energyreductionduetoreducednumberofmessagesends
• Largemessagesrequiremultiplepackets
TestScenario:VehicleTraffic• Routingspace:Twodimensional,50x50kilometerarea• Publicationregions:sensorsattrafficintersections,publishingtrafficvolumedataforvehiclestravelinginnorthandsouthdirection;sensorrangeof400meters
• Subscriptionregions:mobilevehiclesreceivingdatafromsensorsapproximately805meters(0.5miles)fromcurrentlocationincurrentdirectionoftravel
• DDMImplementationapproaches– Centralizedregion-based– Distributedregion-based– Grid-basedimplementation– Optimizedgrid-basedimplementation(filteredgrids)
DDMComputationEnergy
• Computationenergyingrid-basedapproachesnegligible• Region-basedefficientif
– Smallnumberofdevices,or– Infrequentchangestopublicationandsubscriptionregions
• Region-based– O(N2)matchingcomputationto
determinegroupcomposition• DistributedRegion-based
– MultipleDDMservers• Grid-based(Rak,VanHook 1996)
– O(1)computationtodetermineoverlappingcells
• GridFilteredRegionBased(Boukerche etal2005)
– Optimizationtogrid-basedusingcoveragethresholdstoreduceDDMoverheads
0.00001
0.0001
0.001
0.01
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100 200 300 400 500 600 700 800 900 1000
Energy(J)
NumberofDevices
DDM:ComputationEnergy
Regions DistributedRegions FixedGrid FilteredGrid
DDMCommunicationsEnergy
Trafficsimulationscenario• FixedGrid:Irrelevantandduplicatemessages• GridFiltered:AvoidsIrrelevantmessages• Regions:Noirrelevantorduplicatemessages
0.01
0.1
1
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100 200 300 400 500 600 700 800 900 1000
Energy(J)
NumberofDevices
EnergyperStateUpdate
Region/DistributedRegionBased FixedGrid GridFiltered
IntegratedDDMDesign
• ExistingapproachestreatDDMimplementationseparatefromtheapplication
• Integrateddesignenablesreductioninenergyconsumption– Staticvs.dynamicregions– Alignmentofgridreduces/avoidsduplicateandirrelevantmessages
• ApplicationtunableRTIimplementationscanleadtosimplerDDMimplementation,reduceenergyconsumption
0.1
0.12
0.14
0.16
0.18
0.2
60 80 100 120 140
Energy(J)
SubscriptionRegionSize
EnergyperStateUpdate
RegionBased DistributedRegionBased FixedGrid GridFiltered
• Constrainpublicationregionstoasinglecell
• Eliminatesirrelevantandduplicatemessages
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Energy-Aware Dynamic Data-Driven Application Systems for Transportation Monitoring and Analysis
Richard Fujimoto, Georgia Institute of Technology• Accomplishments
– Calibration procedure for data-driven traffic simulations– Energy consumption for data-driven traffic simulations– Energy consumption for synchronization and data distribution services– Realization of initial G-RTI implementation
• Awards– Article selected to 21st Annual Best of Computing list, ACM Computing Reviews Publications
(“Research Challenges in Parallel and Distributed Simulation,” ACM TOMACS)• Publications
– S. Neal, R. M. Fujimoto, “Energy Consumption of HLA Data Distribution Management Approaches,” Winter Simulation Conference, December 2017.
– R. M. Fujimoto, “Power Consumption in Parallel and Distributed Simulations,” Winter Simulation Conference, December 2017.
– R. M. Fujimoto, M. Hunter, A. Biswas, M. Jackson, S. Neal, “Power Efficient Distributed Simulation,” Principles of Advanced Discrete Simulation, May 2017.
– W. Suh, D. Henclewood, G. Angshuman, R. Guensler, M. Hunter, and R. M. Fujimoto, “Dynamic Data Driven Transportation Systems,” Multimedia Tools and Applications, Feb. 2017.
– D. Henclewood, W. Suh, M. O. Rodgers, R. M. Fujimoto. M. P. Hunter, “A Calibration Procedure for Increasing the Accuracy of Microscopic Traffic Simulation Models,” Transactions of the Society for Modeling and Simulation Intl., Vol. 93, No. 1, pp. 35-47, 2017.
– A. Biswas and R. M. Fujimoto, “Energy Consumption of Synchronization Algorithms in Distributed Simulations,” Journal of Simulation, 11(3), pp. 242-252, 2016.
– S. Neal, R. M. Fujimoto, M. Hunter, “Energy Consumption of Data Driven Traffic Simulations,” Winter Simulation Conference, December 2016.
– M. Hunter, A. Biswas, B. Chilukuri, A. Guin, R. Fujimoto, R. Guensler, J. Laval, H. Liu, S. Neal, P. Pecher, M. Rodgers “Energy-Aware Dynamic Data-Driven Distributed Traffic Simulation for Energy and Emissions Reduction,” InfoSymbiotics/DDDAS Conference August 2016.
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Energy-Aware Dynamic Data-Driven Application Systems for Transportation Monitoring and Analysis
Richard Fujimoto, Georgia Institute of Technology
• Coordination, Synergy, and Collaborations– Panel, Winter Simulation Conference, Dec. 2016; R. M. Fujimoto, N. Celik, H.
Damgacioglu, M. Hunter, D. Jin, Y-J. Son, J. Xu, “Dynamic Data Driven Application Systems for Smart Cities and Urban Infrastructures,” Winter Simulation Conference, December 2016.
– Panel, Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Dec. 2017 [follow up on 2016 Workshop for creating a national research agenda in M&S)
– Panel, Winter Simulation Conference, Dec. 2017; R. M. Fujimoto, C. Carothers, A. Ferscha, D. Jefferson, M. Loper, M. Marathe, S. J. E. Taylor, “Computational Challenges in Modeling and Simulation of Complex Systems,” Winter Simulation Conference, December 2017. [organized with Dong Jin]
– Visit to Illinois Institute of Technology (Dong Jin), Winter 2017– “Network Performance Monitoring and Distributed Simulation to Improve
Transportation Energy Efficiency,” (ARPA-E, R. Guensler, PI)– Assistance in updating IEEE 1516 HLA standard (time management services)
• Exposure/Use by other groups– “Smart Cities” demonstration, Atlanta, Georgia September 2017