BigControl:InfrastructureforCollaborativeDeviceSwarms

GuruParulkarOnBehalfof

StanfordPlatformLabandCollaborators

Collaborators

MacSchawagerAeronautics&Astronautics

Mykel KochenderferAeronautics&Astronautics

Balaji PrabhakarElectricalEng/Computer Science

PeterBailisComputerScience

PatHanrahanElectricalEng/Computer Science

ClarificationandDisclaimer!

• Itisabroadinterdisciplinaryresearchprogram

– Participants(will) includeseveralprofessorswithdiverseexpertiseand10sofgraduatestudents

– Weareatthebeginning oftheprogram

– Wehaveavisionandhigh levelideasbutdon’thaveallthesolutionsandanswers

• ClearlyIdon’thavetheexpertisetopresentthebroadresearchagenda

– Sobenicetome!!!

EmergenceofautonomousdevicesTheywillbepartofourlivesinnearfuture

Sensing+Actuation+Computing+AI

AutonomousVehicles:car,truck Quadcopter/Drone/UAV

RobotsPhysicalspaceswithsensors

connectedtothenet

Forallimages,copyrightsapply.

SwarmsofAutonomousDevices• Wewillhaveinnearfuture“swarmsofautonomousdevices”

ownedandoperatedby– Individuals– Corporations– GovernmentagenciesToachieveavarietyoftasks

• Theswarmsofautonomousdeviceswillhavetosharephysicalspacesandcoordinateamongthemselvestoachievetheirtasks

Coordinationandcontrolofswarmsofautonomousdevicesrepresentsahugechallengeandabigopportunity!

P2PApproachtoCoordinationandControl

• P2Pcontroldifficulttoscale,reason&doglobalplanning/optimizationwith• Everycontrolappneedstosolveadistributedsystemproblem• Ithasn’tworkedinothercomputerandnetworkingsystems

WehavedecidednottofocusonP2Pcontrolandcoordination

LogicallyCentralizedControlandCoordination

CentralizedControlandPlanning– Swarmscanaccomplishbiggerandbetterthings

ControlPlatform

App App App

Network

ControlPlatform

App App App

Network

BigControlPlatform

App App App

Network

LocalandRemote(LogicallyCentralized)ControlReflexiveandPlannedControl

Ourkeyassumptions

• Localautonomouscontrolresponsiblefor

– Stabilityandcollision/disasteravoidance–

– Veryfastreactiontime– “reflexivebehavior”

Lotofon-going researchanddevelopment

• Logicallycentralizedcontrol– ourfocus

– Withaglobalviewandforagloballyoptimaloperation forswarm(s)ofdevices

– Relativelyslowerreactiontime– “plannedbehavior”

BigControlPlatform

App App App

Network

GlobalView

LogicallyCentralizedControl:KeyBenefits

• Canusemorecomputinghorsepower

– Forbettercoordination andcontrol

• Canleverageorintegratelargedatasets

– Historicdata

– Potentiallyrealtimesimulation results

• Createsaglobalview

– Helpswithoptimaloperationof swarm(s)

– Simplifiesapplicationdevelopment

– Makesiteasiertoreasonaboutsystem

BigControlPlatform

App App App

Network

GlobalView Historic&Simulation

Data

ImaginethePossibilities• Rushhourcommuteinamajormetroarea

– Coordinationandcontrolofmillioncars,trafficlights,navigation,people,…– Coordinationandcontrolcansignificantlyimprovehighwayutilization,improvecommute

time,andmakethecommutetimemoreproductive

• Alargedistributioncenter– 10sofmillionsofpackagesinabigwarehouses– 10Kormoredrones– Track,organize,movemillionsofpackagesinabigwarehouse

• 3DScanningofstructures,campuses,cities,…– Afleetofdronestocapturepartofstructure/campus/city– Acoordinatedtrajectories toobservethetargetfromdifferentvantagepoints

• Automatedagriculture– Afleetofdronestoobservethousandsofindividualplansdaily– Afleetofcrop-tendingdronestoprovidespecialized services:watering,pruning,spraying

ImaginethePossibilities:DisasterRecoveryAGrandChallenge

• Example:HurricaneSandyorTohuku Earthquake• Millionsofpeopleand10sofsquaremilesaffected

– Cut-offwithoutwater,food,electricity,communication, roads• Afleetoftrucksandboats

– eachwith1000sofdrones,power,wirelessbasestations• Coordinatedsearchwithaswarmofdrones

– Scourtheaffectedregion,assessthedamage,locatesurvivors, identify regionsrequiring immediatehelptoguide therelief

• Mobilizingautomatedemergencyresponsevehicles(ERVs)– Coordinatedwithdrones fornavigationtoareasrequiring relief

• Smallpackagedelivery– Medicalsupplyandfood topeopleandreliefworkers

BIGCONTROLPLATFORM(BCP)

BigControlasaPlatform• BigControlPlatform

– ThisdecadewillbeaboutBigControlasthelastdecadewasaboutBigData

• LikeMapReduce andSpark,wewanttobuildBCPasaplatform– Itwillsolvesignificantproblems– Simpler frameworkfordeveloperstomake

iteasiertowritecontrolappsWithsomeconstraints

• Differentiatingattributes– Scale– Collaborationamongautonomous devices– Lowlatency

BigControlPlatform

App App App

Network

GlobalView

BigControlPlatform(BCP)Requirements

BigControlPlatform

App App App

Network

GlobalView Historic&Simulation

Data

Realtimesensing

Realtimecontrol

• Realtimesensing– Location,velocity, trajectory– Environmentalparameters– StreamingdataHighvolumenoisydata

• HistoricdatainthestoreHugedatasets

• Globalvieworstate– Mostaccuratestateofdevices inthe

physicalspace– Assessment ofthephysical spaceComputationally veryintensive

• Realtimecontrol– Arevisedplan(actions) todevices:

location,velocity, trajectory,whattosense,…

Lowlatencycomputationallyintensivewithpredictability

BCPrequiresaddressingmanytoughchallenges

BCPInternals:DistributedStateManagementBig Control: Infrastructure for Collaborative Device Swarms

cations. BCP will solve problems such as distributed data management, data fusion, and behavior planning,and it will offer simplified frameworks such as a declarative query language for behavior planning.

Wired and Wireless Networks

Distributed State Management,Notifications

Planning & Control

StableTrajectoryTracking

Data Ingestion,Inference Engine

ActiveSensing

AdaptiveOptimal

Scheduling

DeepReinforcmtLearning

Declarative Planning Global Views of Swarm

Devices

Big Control Platform (BCP)

PackageTracking

LargeScale

MappingCommute

ManagementDisasterRelief

Applications

DataReposi-tories

Real-Time

Sensing

Real-Time

Actuation

Figure 1: System overview for managing collaborative deviceswarms.

Figure 1 shows the overall environment formanaging device swarms and the architecture ofBCP. BCP comprises three layers. The lowestlayer is responsible for distributed state manage-ment and communication, including communica-tion with swarm devices and high-performance no-tifications among the BCP elements. The middlelayer provides two modules that will underlie all ap-plications. The first module is responsible for dataingestion and fusion, and includes an inference en-gine to build consistent global views from the noisydata provided by swarm devices. The second mod-ule is a planning engine, whose function for BigControl applications is similar to that of a relationaldatabase for Big Data applications. It will allow ag-gregate behavioral goals to be specified a declara-tive language, and it will then compute detailed de-vice behaviors to implement those goals. The thirdlayer uses these facilities to create domain-specificservices such as active sensing and deep reinforce-ment learning.

BCP will execute on large clusters of servers inboth edge and core datacenters. In order to handleimmense device swarms, it must scale across hundreds or thousands of servers. BCP must also integratewith massive existing backend datastores, such as those used for Big Data applications. Finally, the facilitiesprovided by BCP must be general purpose, so they can be used for many different applications on a singleswarm, as well as applications on different swarms and applications that encompass multiple swarms.

4 ResearchEnabling large-scale end-to-end swarm applications such as the disaster recovery Grand Challenge will

require a diverse collection of research challenges to be solved and integrated, from low-level communi-cation to high-level multi-agent optimization, path planning, and spatial data management. The sectionsbelow summarize our five major research thrusts, consisting of the BCP system, three supporting areas (lowlatency datacenter, network infrastructure, and security), and applications. Because of space limitations, thedescriptions here are necessarily brief; our full proposal will provide more details.4.1 Big Control Platform (BCP)

Most of the research for this Expedition will be focused on designing and implementing the three layersof BCP, which were introduced in Section 3. These layers present very different research challenges, yetthey must all work together to enable Big Control applications.4.1.1 Bottom Layer: Scalable State Management and Notifications

One of the critical functions of a centralized control plane such as BCP is the ability to create, observeand maintain system state and to communicate changes in that state among the elements of the application.Large swarms will have large amounts of state (1TB or more, not including sensing data) and must supportrapid changes in that state (millions of updates per second). Furthermore, this state must be accessible tolarge numbers of servers in order to meet the computational requirements of a large swarm. Big Controlapplications require a different approach to state management than Big Data applications because of the

4

• BCPisademandingdatacenterapplication– Clusterswithlowlatencycommunication

• Distributedstatemanagement– Stateoftheswarmonmanyservers

• 1TB+&millions ofopspersecond

– Differentstatemayrequiredifferentconsistency,replication,anddurability forthebestperformance

– Accessible tomodulesandappsonmanyservers

• Notifications– Eventnotification todifferentcomponents

– Highthroughput,lowlatency,fault-tolerant

• Highavailability– Allowindividualmoduleorservertofail

– In-serviceupgrades

BCPInternals:DataIngestionandInferenceBig Control: Infrastructure for Collaborative Device Swarms

cations. BCP will solve problems such as distributed data management, data fusion, and behavior planning,and it will offer simplified frameworks such as a declarative query language for behavior planning.

Wired and Wireless Networks

Distributed State Management,Notifications

Planning & Control

StableTrajectoryTracking

Data Ingestion,Inference Engine

ActiveSensing

AdaptiveOptimal

Scheduling

DeepReinforcmtLearning

Declarative Planning Global Views of Swarm

Devices

Big Control Platform (BCP)

PackageTracking

LargeScale

MappingCommute

ManagementDisasterRelief

Applications

DataReposi-tories

Real-Time

Sensing

Real-Time

Actuation

Figure 1: System overview for managing collaborative deviceswarms.

Figure 1 shows the overall environment formanaging device swarms and the architecture ofBCP. BCP comprises three layers. The lowestlayer is responsible for distributed state manage-ment and communication, including communica-tion with swarm devices and high-performance no-tifications among the BCP elements. The middlelayer provides two modules that will underlie all ap-plications. The first module is responsible for dataingestion and fusion, and includes an inference en-gine to build consistent global views from the noisydata provided by swarm devices. The second mod-ule is a planning engine, whose function for BigControl applications is similar to that of a relationaldatabase for Big Data applications. It will allow ag-gregate behavioral goals to be specified a declara-tive language, and it will then compute detailed de-vice behaviors to implement those goals. The thirdlayer uses these facilities to create domain-specificservices such as active sensing and deep reinforce-ment learning.

BCP will execute on large clusters of servers inboth edge and core datacenters. In order to handleimmense device swarms, it must scale across hundreds or thousands of servers. BCP must also integratewith massive existing backend datastores, such as those used for Big Data applications. Finally, the facilitiesprovided by BCP must be general purpose, so they can be used for many different applications on a singleswarm, as well as applications on different swarms and applications that encompass multiple swarms.

4 ResearchEnabling large-scale end-to-end swarm applications such as the disaster recovery Grand Challenge will

require a diverse collection of research challenges to be solved and integrated, from low-level communi-cation to high-level multi-agent optimization, path planning, and spatial data management. The sectionsbelow summarize our five major research thrusts, consisting of the BCP system, three supporting areas (lowlatency datacenter, network infrastructure, and security), and applications. Because of space limitations, thedescriptions here are necessarily brief; our full proposal will provide more details.4.1 Big Control Platform (BCP)

Most of the research for this Expedition will be focused on designing and implementing the three layersof BCP, which were introduced in Section 3. These layers present very different research challenges, yetthey must all work together to enable Big Control applications.4.1.1 Bottom Layer: Scalable State Management and Notifications

One of the critical functions of a centralized control plane such as BCP is the ability to create, observeand maintain system state and to communicate changes in that state among the elements of the application.Large swarms will have large amounts of state (1TB or more, not including sensing data) and must supportrapid changes in that state (millions of updates per second). Furthermore, this state must be accessible tolarge numbers of servers in order to meet the computational requirements of a large swarm. Big Controlapplications require a different approach to state management than Big Data applications because of the

4

• Sensingdataishighvolumeandnoisy

• Ingestionmustrapidly

– heal/curethedata,indexit,combine itwithotherrelevantdatasuchasmaps,pointsofinterest,etc,andrender it

– Inferencetodetectandreportanomalies

Toconstructtheglobalview

• Overallapproachinvolvesreconstructingspatio-temporalprocessesfromnoisysnapshots

• Leveragedistributedstatemanagementandlowlatencycommunication

BCPInternals:DeclarativePlanningBig Control: Infrastructure for Collaborative Device Swarms

cations. BCP will solve problems such as distributed data management, data fusion, and behavior planning,and it will offer simplified frameworks such as a declarative query language for behavior planning.

Wired and Wireless Networks

Distributed State Management,Notifications

Planning & Control

StableTrajectoryTracking

Data Ingestion,Inference Engine

ActiveSensing

AdaptiveOptimal

Scheduling

DeepReinforcmtLearning

Declarative Planning Global Views of Swarm

Devices

Big Control Platform (BCP)

PackageTracking

LargeScale

MappingCommute

ManagementDisasterRelief

Applications

DataReposi-tories

Real-Time

Sensing

Real-Time

Actuation

Figure 1: System overview for managing collaborative deviceswarms.

Figure 1 shows the overall environment formanaging device swarms and the architecture ofBCP. BCP comprises three layers. The lowestlayer is responsible for distributed state manage-ment and communication, including communica-tion with swarm devices and high-performance no-tifications among the BCP elements. The middlelayer provides two modules that will underlie all ap-plications. The first module is responsible for dataingestion and fusion, and includes an inference en-gine to build consistent global views from the noisydata provided by swarm devices. The second mod-ule is a planning engine, whose function for BigControl applications is similar to that of a relationaldatabase for Big Data applications. It will allow ag-gregate behavioral goals to be specified a declara-tive language, and it will then compute detailed de-vice behaviors to implement those goals. The thirdlayer uses these facilities to create domain-specificservices such as active sensing and deep reinforce-ment learning.

BCP will execute on large clusters of servers inboth edge and core datacenters. In order to handleimmense device swarms, it must scale across hundreds or thousands of servers. BCP must also integratewith massive existing backend datastores, such as those used for Big Data applications. Finally, the facilitiesprovided by BCP must be general purpose, so they can be used for many different applications on a singleswarm, as well as applications on different swarms and applications that encompass multiple swarms.

4 ResearchEnabling large-scale end-to-end swarm applications such as the disaster recovery Grand Challenge will

require a diverse collection of research challenges to be solved and integrated, from low-level communi-cation to high-level multi-agent optimization, path planning, and spatial data management. The sectionsbelow summarize our five major research thrusts, consisting of the BCP system, three supporting areas (lowlatency datacenter, network infrastructure, and security), and applications. Because of space limitations, thedescriptions here are necessarily brief; our full proposal will provide more details.4.1 Big Control Platform (BCP)

Most of the research for this Expedition will be focused on designing and implementing the three layersof BCP, which were introduced in Section 3. These layers present very different research challenges, yetthey must all work together to enable Big Control applications.4.1.1 Bottom Layer: Scalable State Management and Notifications

One of the critical functions of a centralized control plane such as BCP is the ability to create, observeand maintain system state and to communicate changes in that state among the elements of the application.Large swarms will have large amounts of state (1TB or more, not including sensing data) and must supportrapid changes in that state (millions of updates per second). Furthermore, this state must be accessible tolarge numbers of servers in order to meet the computational requirements of a large swarm. Big Controlapplications require a different approach to state management than Big Data applications because of the

4

• Providehigherlevelabstractionstoappdevelopers

– Whatvs.how

– E.g.AskforIRreadingsfromspecificlocationvs.precisecommandsforindividualdevicestosurveyagivenarea

• PlanningEnginetranslateshigherleveldeclarativeplantospecificcommandsforvariousdevices

– Similartoqueryplanning inrelationaldatabases

BCPInternals:AppServicesBig Control: Infrastructure for Collaborative Device Swarms

cations. BCP will solve problems such as distributed data management, data fusion, and behavior planning,and it will offer simplified frameworks such as a declarative query language for behavior planning.

Wired and Wireless Networks

Distributed State Management,Notifications

Planning & Control

StableTrajectoryTracking

Data Ingestion,Inference Engine

ActiveSensing

AdaptiveOptimal

Scheduling

DeepReinforcmtLearning

Declarative Planning Global Views of Swarm

Devices

Big Control Platform (BCP)

PackageTracking

LargeScale

MappingCommute

ManagementDisasterRelief

Applications

DataReposi-tories

Real-Time

Sensing

Real-Time

Actuation

Figure 1: System overview for managing collaborative deviceswarms.

Figure 1 shows the overall environment formanaging device swarms and the architecture ofBCP. BCP comprises three layers. The lowestlayer is responsible for distributed state manage-ment and communication, including communica-tion with swarm devices and high-performance no-tifications among the BCP elements. The middlelayer provides two modules that will underlie all ap-plications. The first module is responsible for dataingestion and fusion, and includes an inference en-gine to build consistent global views from the noisydata provided by swarm devices. The second mod-ule is a planning engine, whose function for BigControl applications is similar to that of a relationaldatabase for Big Data applications. It will allow ag-gregate behavioral goals to be specified a declara-tive language, and it will then compute detailed de-vice behaviors to implement those goals. The thirdlayer uses these facilities to create domain-specificservices such as active sensing and deep reinforce-ment learning.

BCP will execute on large clusters of servers inboth edge and core datacenters. In order to handleimmense device swarms, it must scale across hundreds or thousands of servers. BCP must also integratewith massive existing backend datastores, such as those used for Big Data applications. Finally, the facilitiesprovided by BCP must be general purpose, so they can be used for many different applications on a singleswarm, as well as applications on different swarms and applications that encompass multiple swarms.

4 ResearchEnabling large-scale end-to-end swarm applications such as the disaster recovery Grand Challenge will

require a diverse collection of research challenges to be solved and integrated, from low-level communi-cation to high-level multi-agent optimization, path planning, and spatial data management. The sectionsbelow summarize our five major research thrusts, consisting of the BCP system, three supporting areas (lowlatency datacenter, network infrastructure, and security), and applications. Because of space limitations, thedescriptions here are necessarily brief; our full proposal will provide more details.4.1 Big Control Platform (BCP)

Most of the research for this Expedition will be focused on designing and implementing the three layersof BCP, which were introduced in Section 3. These layers present very different research challenges, yetthey must all work together to enable Big Control applications.4.1.1 Bottom Layer: Scalable State Management and Notifications

One of the critical functions of a centralized control plane such as BCP is the ability to create, observeand maintain system state and to communicate changes in that state among the elements of the application.Large swarms will have large amounts of state (1TB or more, not including sensing data) and must supportrapid changes in that state (millions of updates per second). Furthermore, this state must be accessible tolarge numbers of servers in order to meet the computational requirements of a large swarm. Big Controlapplications require a different approach to state management than Big Data applications because of the

4

• ActiveSensing– Quantifyprecisionofcurrentknowledgeand

takeactionstoimproveit– Requiresfusionofdatafrommultiplesensors–

off-linealgorithmstoday– Ourgoalistoexploreon-linealgorithms

• AdaptiveOptimalScheduling– Overallplanfortheswarm– Leveragesreinforcementlearning

• DeepReinforcementLearning– Tocopewithmanyuncertainties

• StableTrajectoryTracking– Basedonfeedbackfromactualsenseddata– Incrementalcontroltostayonstabletrajectory

BCPRequiresLowLatencyDataCenter

• End-to-endlatency~10ms• Apps+BCPcomputationonserversshould

belowlatencyandpredictable• Low-LatencyRPC:1-10usec

– Newtransportprotocolwithareceiverdrivencongestionandflowcontrol

– Kernelby-pass– Polling toavoidinterruptoverheads– Minimalstateforscalability

• Core-awarethreadscheduling– Kerneldoesscheduling ofcores– Applicationdoesitsownthreadscheduling

BigControlPlatform

App App App

Network

GlobalView

BCPRequiresLowLatencyDataCenter

Lowlatencystorage• Currentmemoryabstractionsoptimizedfor

limitedmemory,hightemporallocality,andslowpersistentstorage

• Flashandnon-volatilememoryprovidefasthighcapacitystorage

• Newmemoryabstractions(singlelevel),APIsandstoragerepresentationsforfastaccess

BigControlPlatform

App App App

Network

GlobalView

SoftwareDefinedNetworksForLowLatencyandPredictability

• Bigcontrolrequiresnetworkstobetunedfor“control”– Lowlatency(1ms)andpredictability– Highly reliableconnectivity

• Softwaredefinednetworks– Programmabledataandcontrolplanesto

achievelowlatencywithQoS orpredictability

– Slicingfordifferentappstoallowisolationandcustomization

– Eachslicecanbeprogrammedtoachieveitsperformance targets

BigControlPlatform

App App App

Network

GlobalView

Security

• Secureconnectivity– Protectcommunicationamongswarmparticipantsfromtempering– Eachpacketcannotbedigitallysigned– tooexpensive– Designnewswarm-integrityandsimplekeymanagement techniques

• Protectionagainstdronesensorattacks– Sensorattacks:disablesensorsonDrones(sound wavestodisablegyroscope, GPS

spoofing todisablepositioning system)– Swarmcollectivescanfindwaystoberesilienttosensorattacks(byzantinefailureson

asmallsetofdrones)– Designrelevantsynchronizationandbyzantinefaulttolerantprotocols

WhyStanfordPlatformLab?• OurDNAistotakeonsuchchallengingprogramswithpotentialforhugeimpact• Aninterdisciplinary researchagendarequires

– Breadthanddepthofexpertiseinmultiple fields– easytofindatStanford– Combinationoffoundational researchandscalablehighperformancesystems–

something wehaveahistoryofdoing• Wehaveastrongcollaborationwiththeindustry

– Cloudproviders, vendors,andother stakeholders– Togetherwecancreategeneralpurposeplatformsandsolutions fortheindustry to

useandbuildon• Expectedresults

– Architectures,abstractions,algorithmsandotherfundamental contributions– Opensourceplatformsandcommunities toenablemoreinnovation inthisarea– Successfultechnology transfertoimpactpracticeofthefield– Integrationofresearchandeducation

Summary

• BigControlisanexcitingopportunitytodefineanewcomputingparadigmthatwillenableapplicationswithfar-reachingimpactsonthesociety

• BigControlanditsapplicationsposemanytoughchallengesthatareworthyofourfocusforthenextfewyears

• Excitingtimesahead…

• Timetojumpinwithbothfeet!!

BreakoutSessions

• FirstBreakoutSession– ApplicationsofBCP

• SecondBreakoutSession– Candidatestartupprojects– BCPTechnologiesandComponents

• DistributedStateManagement• Dataingestionandinference• Declarativeprogramminglanguagesforplanningandcontrol

– Lowlatencydatacenter– SDNforlowlatencyandQOS– End-to-endcontrol– whatitwouldlooklike(latencybudgets)

• Doashowofhands