Deploying data streaming applicaons in the...

Deployingdatastreamingapplica2onsintheFog

ValeriaCardellini

cardellini@ing.uniroma2.it

UniversityofRomeTorVergata

2ndWorkshop“ThroughtheFog”–Pisa,Italy

Datastreamprocessing(DSP)

•  Avarietyoflow-latencyandlocaLon-awareapplicaLonsindiversedomains:–  SituaLon-awareapplicaLons(e.g.,intelligenturbantransport,surveillance,andtrafficcongesLon)

–  Socialdatamining

•  Require–  ConLnuousreal-2meprocessingofunboundeddatastreamsgeneratedbymulLple,distributedsources

–  ToextractvaluableinformaLoninaLmelyandreliablemanner

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Inanewdistributedenvironment•  Toincreasescalabilityandavailability,reducelatency,networktraffic,andpowerconsumpLon

– Edge/fogcompu2ng(“thecloudclosetotheground”):manymicrodatacenterslocatedatthenetworkedge

Exploitdistributedandnear-edgecomputaLon

…thatposesoldandnewchallenges

•  Networklatenciesaresignificant•  CompuLngandnetworkingresourcesareheterogeneous(e.g.,businessconstraints,capacitylimits,…)

•  CompuLngandnetworkresourcesarenotalwaysavailable

•  Datacannotbeprocessedeverywhere•  …

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Goalofthetalk

•  GiveaflavorofsomechallengesandtheirpossiblesoluLonsthatarisewhendeployingdatastreamprocessingapplicaLonsinafog/edgeenvironment

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DSPapplicaLonbasics

•  Anetworkofoperatorsconnectedbydatastreams,atleastonedatasourceandonedatasink

•  Representedbyadirectedgraph–  GraphverLces:operators–  Graphedges:datastreams–  Usuallydirectedacyclicgraph(DAG)

•  Operator:–  Processingelementthattransformsoneormoreinputstreamsintoanotherstream

–  Canbestatelessorstateful5V.Cardellini-ThroughtheFog2017

Challenge1:Operatorplacement

•  HowtoassigntheDSPoperatorstocompuLngnodeswhicharedistributedinaFogenvironment

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1 23

4 6

5

(1,2)

(1,2) (1,2) (2,3)(2,4)

(3,5)(4,5)

(4,6)

(4,6)

(2,4)(2,3)

(3,5)

(4,5)

(4,6)

Thebeginning:DistributedStorm

•  CurrentDSPsystems(e.g.,Storm,Flink,Heron)aredesignedtoruninsingledatacenters

•  OuriniLalgoal:toextendStormforalarge-scaledistributedandheterogeneousenvironment

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V.Cardellini,V.Grassi,F.LoPres2,M.Nardelli,DistributedQoS-awareschedulinginStorm.DEBS’15.V.Cardellini-ThroughtheFog2017

NetworklatencyesLmaLon

•  HowtoprovideanefficientesLmaLonofthenetworkdelaybetweenpairsofnodes?

•  Useanetworkcoordinatessystem– Topredictlatencieswithoutperformingdirectmeasurements•  E.g.,Vivaldinetworkcoordinates:decentralizedandgossip-basedscheme

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Operatorplacementpolicies

•  Operatorplacement:NP-hardproblem•  Severalplacementpoliciesinliterature(mainlyheurisLcs)thataddresssuchproblembut– DifferentassumpLons(systemmodel,applicaLontopology,QoSahributesandmetrics,…)

– DifferentobjecLves– Noteasilycomparable

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ODP:OpLmalDSPPlacement•  WeproposeODP–  CentralizedpolicyforopLmalplacementofDSPapplicaLons

–  FormulatedasIntegerLinearProgramming(ILP)problem

•  Ourgoals:–  Tocomputetheop2malplacement(ofcourse!)

–  Toprovideaunifiedgeneralformula2onoftheplacementproblemforDSPapplicaLons(butnotonly!)

–  ToconsidermulLpleQoSaNributesofapplicaLonsandresources

–  ToprovideabenchmarkforheurisLcs

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V.Cardellini,V.Grassi,F.LoPres2,M.Nardelli,Op2malOperatorPlacementforDistributedStreamProcessingApplica2ons,DEBS’16. V.Cardellini-ThroughtheFog2017

ODP:modelDSPapplica2on

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Operators• CirequiredcompuLngresources• RiexecuLonLmeperdataunit

Datastreams• λi,j dataratefromoperatoritoj

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ODP:modelCompu2ngandnetworkresources

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(Logical)Networklinks• du,vnetworkdelayfromutov

• Bu,v bandwidthfromutov

• Au,vlinkavailability

Compu2ngresources• Cuamountofresources

• Su processingspeed• Auresourceavailability

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DecisionvariablesWheretomapoperatorsanddatastreams

OpLmalDSPPlacementModel

i

j

xi,u=1

y(i,j),(u,v)=1

xj,v=1

u

z

v

w

ODP:model

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ODP:someQoSmetrics

•  Latency– Maxend-to-enddelaybetweensourcesanddesLnaLons

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R

•  Availability–  Prob.thatalloperators/linksareupandrunning

•  Latencyandbandwidth–  Inter-nodetraffic–  Networkusage

•  Inflightbytes Σlinks∈lrate(l)Lat(l)V.Cardellini-ThroughtheFog2017

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Latency

Availability

Networkbandwidthandnodecapacityconstraints

Assignmentandintegerconstraints

ODP:OpLmalDSPPlacementModelODP:ILPformulaLonTunableknobstosettheopLmalplacementgoals

V.Cardellini-ThroughtheFog2017

ODPandApacheStorm•  WecanuseODP

–  todeterminetheopLmalplacement

–  asbenchmarktoevaluateexisLngheurisLcs

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ODP

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ODP:BenchmarkforplacementheurisLcsDistributedplacementheurisLcthatminimizesnetworkusage

17P.Pietzuchetal.,Network-awareoperatorplacementforstream-processingsystems,ICDE‘06.

Pietzuchetal.:

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Challenge2:placementandreplicaLon

•  ExploitapplicaLon-levelparallelismbyreplicaLonoperators

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A B

A

A

A

Split Merge

OperatorplacementandreplicaLon

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ODRP:OpLmalDSPReplicaLonandPlacement•  WeproposeODRP

–  CentralizedpolicyforopLmalreplicaLonandplacementofDSPapplicaLons

–  FormulatedasIntegerLinearProgramming(ILP)problem

•  Ourgoals:–  TojointlydeterminetheopLmalnumberofreplicaandtheirplacement

–  ToconsidermulLpleQoSaNributesofapplicaLonsandresources

–  Toprovideaunifiedgeneralformula2on

–  ToprovideabenchmarkforheurisLcs

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V.Cardellini,V.Grassi,F.LoPres2,M.Nardelli,Op2maloperatorreplica2onandplacementfordistributedstreamprocessingsystems.ACMPerf.Eval.Rew.,2017. V.Cardellini-ThroughtheFog2017

ODRPperformance

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sinkoperatorsource

RabbitMQRedis

data source parser filterByCoordinates

metronome

computeRouteID

partialRankcountByWindow globalRank

0.001

0.01

0.1

1

10

100

1000

20 40 60 80 100 120

Response time (s)

Source data rate (tuples/s)

S-ODP_RS-ODRP_R

DSPapplicaLon:DEBS2015GrandChallenge

Challenge3:runLmedeployment

•  ManyfactorsmaychangeatrunLme,e.g.,–  LoadvariaLons,QoSahributesofresources,costofresources(e.g.,duetodynamicpricingschemes),networkcharacterisLcs,nodemobility,…

•  HowtoadapttheplacementandreplicaLonwhenchangesoccur?Exploitself-adap2vedeployment

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Self-adapLvedeployment

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•  MAPE(Monitor,Analyze,PlanandExecute)

•  Planphase:howtoreconfiguretheapplicaLondeployment

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ReconfiguraLonchallenges

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•  Reconfiguringthedeploymenthasanonnegligiblecost!

•  CanaffectnegaLvelyapplicaLonperformanceintheshortterm–  ApplicaLonfreezingLmescausedbyoperator

migraLonandscaling,especiallyforstatefuloperators

PerformreconfiguraLononlywhenneeded

TakeintoaccounttheoverheadformigraLngandscalingtheoperators

V.Cardellini-ThroughtheFog2017

ElasLcstatefulmigraLoninStorm

•  WedevelopmechanismsforelasLcstatefulmigraLoninApacheStorm

Supervisor Supervisor Supervisor Supervisor

worker

process

worker

process

worker

slot

worker

slot

worker

slot

worker

slot

worker

process

worker

process

worker

process

worker

process

worker

process

worker

process

DDS DDS DDS DDS

Network

schedulerMigrationNotifier

ElasticityManager

Nimbus ZooKeeper

25V.Cardellini-ThroughtheFog2017V.Cardellini,M.Nardelli,D.Luzi,Elas2cstatefulstreamprocessinginStorm,HPCS‘16.

EDRP:ElasLcDSPReplicaLonandPlacement

•  UnifiedframeworkfortheQoS-awareiniLaldeploymentandrunLmeelasLcitymanagementofDSPapplicaLons

•  Wemodelreconfigura2oncosts–  RelatedtomigraLngorscalingin/outtheoperators

•  CentralizedpolicyformulatedasIntegerLinearProgramming(ILP)problem

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V.Cardellini,F.LoPres2,M.Nardelli,G.RussoRusso,Op2maloperatordeploymentandreplica2onforelas2cdistributeddatastreamprocessing,underreview,2017.

EDRPperformance

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WithreconfiguraLon

penalLes:Availability:95.5%DownLme:<90s

WithoutreconfiguraLon

penalLes:Availability:81.5%DownLme:370s

Futurework•  StudyefficientheurisLcstodealwithlargeprobleminstances

•  Dealwithuncertainty:takeuncertaintyofparametersintoaccountanddesignrobustplacementalgorithms

•  StudyhowtodeploymulLplecompeLngapplicaLonsintheFog

•  IntegrateplacementdecisionwithSDN– WithSDN,networkintothecontrolloop

•  Studycross-layerstrategiesthatinvolvemulLpleBigdataframeworksintheFog–  E.g.,Heron+ApacheAurora+Mesos

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Acknowledgments–Co-authors

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VincenzoGrassi FrancescoLoPresL MaheoNardelli

Thankyou!AnyquesLons?

ValeriaCardellinicardellini@ing.uniroma2.it

hhp://www.ce.uniroma2.it/~valeriaV.Cardellini-ThroughtheFog2017