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Copyright © 2016 Splunk Inc. Alex Cruise Sr. Dev. Manager/Architect, Splunk Fred Zhang Sr. Data Scientist, Splunk Machine Learning and Anomaly Detection in Splunk IT Service Intelligence

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Page 1: Machine Learning and Anomaly Detection in SplunkIT Service ......Machine Learning and Anomaly Detection in SplunkIT Service Intelligence. Disclaimer 2 ... Uses new Chunked External

Copyright©2016Splunk Inc.

AlexCruiseSr.Dev.Manager/Architect,SplunkFredZhangSr.DataScientist,Splunk

MachineLearningandAnomalyDetectioninSplunk ITServiceIntelligence

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Disclaimer

2

Duringthecourseofthispresentation,wemaymakeforwardlookingstatementsregardingfutureeventsortheexpectedperformanceofthecompany.Wecautionyouthatsuchstatementsreflectourcurrentexpectationsandestimatesbasedonfactorscurrentlyknowntousandthatactualeventsorresultscoulddiffermaterially.Forimportantfactorsthatmaycauseactualresultstodifferfromthose

containedinourforward-lookingstatements,pleasereviewourfilingswiththeSEC.Theforward-lookingstatementsmadeinthethispresentationarebeingmadeasofthetimeanddateofitslivepresentation.Ifreviewedafteritslivepresentation,thispresentationmaynotcontaincurrentoraccurateinformation.Wedonotassumeanyobligationtoupdateanyforwardlookingstatementswemaymake.Inaddition,anyinformationaboutourroadmapoutlinesourgeneralproductdirectionandissubjecttochangeatanytimewithoutnotice.Itisforinformationalpurposesonlyandshallnot,beincorporatedintoanycontractorothercommitment.Splunkundertakesnoobligationeithertodevelopthefeaturesor

functionalitydescribedortoincludeanysuchfeatureorfunctionalityinafuturerelease.

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Agenda

Introductions/HistoryAxioms– ProblemDomainAxioms– SolutionDomainTimeSeriesFeatureEngineeringSpatialvs.TemporalAnalysisOtherApproachesMADServiceEngineeringITSIContext

3

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Introductions/History

Keyteammembers– Shang– Mihai– Jacob– Iman– Touf

Presenters– Fred– Datascientist– Alex– Architect/DevManager

4

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Axioms– ProblemDomain

5

THEUNIVERSEOFDATA

Time-seriesdata

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Axioms– ProblemDomain

6

THEUNIVERSEOFDATA

ENHANCE!

Time-seriesdata

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Axioms– ProblemDomain

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Detectinganomaliesinthisnarrowsubsetoftheuniverseofdata:TimeseriesNumericvariablesthatchangeovertime

IncreasingTimeà

x

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Axioms– ProblemDomain

8

Detectinganomaliesinthisnarrowsubsetoftheuniverseofdata:TimeseriesNumericvariablesthatchangeovertime

Regular timeseriesThenewvaluesarriveonaregularinterval(e.g.everyfiveseconds)

IncreasingTimeà

x

regularinterval

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Axioms– ProblemDomain

9

Detectinganomaliesinthisnarrowsubsetoftheuniverseofdata:TimeseriesNumericvariablesthatchangeovertime

Regular timeseriesThenewvaluesarriveonaregularinterval(e.g.everyfiveseconds)

Dense,RegulartimeseriesNewvaluesarefairlylikelytoarriveandnotbenull

IncreasingTimeà

x

regularinterval

fewgaps/nulls/NaNs

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Axioms– SolutionDomain

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UnsupervisedNon-ParametricRobustStreamingAdaptiveDomain-agnostic

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Axioms– SolutionDomain

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Unsupervised– Nolabelledanomalies– What’snormalislearnedfromobservingthedataitself,notdefinedbyan

expertNon-ParametricRobustStreamingAdaptiveDomain-agnostic

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Axioms– SolutionDomain

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UnsupervisedNon-Parametric– Wemakenoassumptionsabouttheprobabilitydistributionofthevalues

(e.g.Gaussianorstationary)

RobustStreamingAdaptiveDomain-agnostic

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Axioms– SolutionDomain

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UnsupervisedNon-ParametricRobust– Outliersaredetectedasanomalies,butdon’tcausedistortionsinour

expectations

StreamingAdaptiveDomain-agnostic

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Axioms– SolutionDomain

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UnsupervisedNon-ParametricRobustStreaming– Noseparatetraining/testperiods– Anomaliesaredetectedandreportedin(near-)realtime

AdaptiveDomain-agnostic

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Axioms– SolutionDomain

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UnsupervisedNon-ParametricRobustStreamingAdaptive– Nostaticthresholds,discovernormalbehaviourpatternsautomatically– Adapttobehavioralchangeswithoutend-userfeedback– WhatwasnormallastweekmightbeworrisometodayDomain-agnostic

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Axioms– SolutionDomain

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UnsupervisedNon-ParametricRobustStreamingAdaptiveDomain-agnostic– Purelynumeric– Noinformationaboutunderlyingsubjectsorcausesofthebehaviourstream

Memory/CPUusage

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Axioms– SolutionDomain

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UnsupervisedNon-ParametricRobustStreamingAdaptiveDomain-agnostic– Purelynumeric– Noinformationaboutunderlyingsubjectsorcausesofthebehaviourstream

Unicornspersecond

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GettingDataIn

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Ifyoualreadyhavedense,regular,numerictimeseries(aka“metrics”or“KPIs”)you’regoodtogo

TimeSeriesFeatureEngineering

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GettingDataIn

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Ifyoualreadyhavedense,regular,numerictimeseries(aka“metrics”or“KPIs”)you’regoodtogoIfyouhavesomethingelse,nowyouhaveatimeseriesfeatureengineeringproblem

TimeSeriesFeatureEngineering

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GettingDataIn

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Ifyoualreadyhavedense,regular,numerictimeseries(aka“metrics”or“KPIs”)you’regoodtogoIfyouhavesomethingelse,nowyouhaveatimeseriesfeatureengineeringproblemThereareinescapabletradeoffsbetweendensity andprecision

TimeSeriesFeatureEngineering

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GettingDataIn

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Ifyoualreadyhavedense,regular,numerictimeseries(aka“metrics”or“KPIs”)you’regoodtogoIfyouhavesomethingelse,nowyouhaveatimeseriesfeatureengineeringproblemThereareinescapable tradeoffsbetweendensity andprecisionIncreasedprecisionimpliessparsertimeseries– Alsoincreasedmemoryandbandwidthusage!

TimeSeriesFeatureEngineering

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GettingDataIn

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Ifyoualreadyhavedense,regular,numerictimeseries(aka“metrics”or“KPIs”)you’regoodtogoIfyouhavesomethingelse,nowyouhaveatimeseriesfeatureengineeringproblemThereareinescapable tradeoffsbetweendensity andprecisionIncreasedprecisionimpliessparsertimeseries– Alsoincreasedmemoryandbandwidthusage!

TSFErequiresdealingwithTime,Space andValues

TimeSeriesFeatureEngineering

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GettingDataIn

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Time– Howfrequently donewvaluesarrive?– Howregularly donewvaluesarrive?– Howprecisely dowewanttobeabletorecordthetimewhenthe

measurementwastaken?ê Finertimeresolutionincreasessparsity:theprobabilitythatanyeventoccurredduringaparticulartimewindowisdecreased

SpaceValues

TimeSeriesFeatureEngineering

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GettingDataIn

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TimeSpace- howprecisely dowewanttobeabletorelatetimeseriesbacktotheunderlyingeventstream?

ê Howmanydimensions?e.g.IPaddress,geo.coordinates,MIMEtype,HTTPresponsecode– Addingdimensionsincreasesprecision,butalsomagnifiesthelikelihoodofsparsity

ê Withinadimension,howprecisedoweneedtobe?– FullIPaddressor/24?Distinguish400,401,403,404orjust4xx?– Country,state/province,city,neighbourhood,building,…?– Extraprecisionincreasesthelikelihoodofsparsity

Values

TimeSeriesFeatureEngineering

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GettingDataIn

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TimeSpaceValues– Howdowegenerateanumber?

ê Getanumericfieldas-is(i.e.a“gauge”)ê Incrementacounter

– Howdoweaggregatemultiplevalues?ê Min,max,mean,etc.

– Howshouldwehandlemissingvalues?ê ”Replacenullwithzero”onlymakessenseforsomethingweknowisacounterê “Takethepreviousvalue”mightmakesense

TimeSeriesFeatureEngineering

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MetricAnomalyDetectionAlgorithms

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Proprietary!Notopensourceoroff-the-shelf.Spatialandtemporalalgorithms– Whatdowemeanby“spatial”and“temporal”?– Completelyorthogonal,irreducibledistinction

ê Onecannotsubstitutefortheotherê Neitherisalwaysapplicabletoeverytimeseries

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MetricAnomalyDetectionAlgorithms

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Analyzeonetimeseriesatatime(embarrassinglyparallel)Alertingwhenpresentbehaviourissurprisingcomparedtopastbehaviour

TemporalAnalysis(aka“Trending”algorithm)

IncreasingTimeà

xnowß past

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MetricAnomalyDetectionAlgorithms

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Goodresultsonlywhenthereisahistoryofrecurringpatternsintheunderlyingeventstream– Notnecessarilyperiodic,justrecurring

Howmuchhistory?– Preliminary(usuallybad)resultsafter~2000points

ê e.g.1.5days at1-minuteresolution– Greatresultsaftera“fullperiod”hasbeenobserved(e.g.7days)– Moreisbetter!(modulomemory,storage…)

TrendingAlgorithmConstraints

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MetricAnomalyDetectionAlgorithms

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Comparepresent behaviourofmultiplemetrics

Spatial(“Cohesive”)Algorithm

IncreasingTimeà

x now

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MetricAnomalyDetectionAlgorithms

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Givenaset*oftimeseriesthatareexpected†tobehavesimilarly‡,detectwhenoneormoreofthemdepartsfromtheirpeers

*set>=3members

†expectedbyahumananalystorinterestingMLprocess

‡similarlyRoughlythesameshapeScaleandmagnitudeinvariant

CohesiveAlgorithmConstraints

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MetricAnomalyDetectionAlgorithms

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NoperiodicityrequiredHistoryimprovesscale/magnitudeinvariancePerformancereliesonsimilaritywithingroup– Whatifthegroupisn’tinherentlycohesive?

ê Lotsofalertsearlyonê Then,thealgorithmadaptstothechaosê Ifthegroupreturnstocohesion,thealgorithmwillautomaticallyadapttothe“newnormal”.

CohesiveAlgorithmCharacteristics

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MetricAnomalyDetectionAlgorithms

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Aclusterofserversperformingasimilarroleforthesameapplication,behindthesameloadbalancerAssumingtheloadbalancerisoperatingnominally,manyservermetricsshouldberoughlycorrelated,e.g.:– CPUusage(user,system,idle)– Diskusage(reads,writes,IOPS)– Networkusage(bandwidth,#activesockets)– Application-specificmetrics(requestshandledpersecond,500errors,

authenticationfailures,activesessions)

CohesiveAlgorithm:ExampleUseCase#1

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MetricAnomalyDetectionAlgorithms

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ImaginesomewindturbinesonthesamehillWecan’tpredictwinddirectionandspeedverywell(yet?)Butweexpecteveryturbineshouldberoughlycohesiveinseveralmetrics:– rotationspeed– powergenerationrate– vibration– direction

ê *actually,becausethisisaperiodicmetric(359° ≈1°),wedon’tsupportitwellrightnow

Ifanymetricforanyturbinedifferssignificantlyfromitspeers,weshouldbenotified,andmaybesendateamtoinvestigate

CohesiveAlgorithm:ExampleUseCase#2

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Otherapproacheswehavetried

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3-sigmaKolmogorov-SmirnovtestoverslidingwindowsTime-seriesforecastingmethods– Holt-Winters(previousversionofITSIADisbasedonitsnon-parametricversion)– ARIMA,etc

One-classSVMClusteringmethods– DBSCAN,K-means,etcVariousR,Pythonpackages

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MADServiceEngineering

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MAD=“MetaforAnomalyDetection”

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MADServiceEngineering

36

MAD=“Metafor AnomalyDetection”

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MADServiceEngineering

37

MAD=“Metric AnomalyDetection”

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MADServiceEngineering

38

MAD=“Metric AnomalyDetection”WritteninScala– usingAkkaforconcurrency

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MADServiceEngineering

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MAD=“Metric AnomalyDetection”WritteninScala– usingAkkaforconcurrency

UsesSearchCommandProtocolv2(availablesinceSplunk6.3)– Runsforever,doesn’tgetrestartedevery50kevents– Receivesdatasoonafteritarrivesatanindexer,nopolling

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MADServiceEngineering

40

MAD=“Metric AnomalyDetection”WritteninScala– usingAkkaforconcurrency

UsesnewChunkedExternalCommandfeatureofSplunk6.3– Runsforever,doesn’tgetrestartedevery50kevents– Receivesdatasoonafteritarrivesatanindexer,nopolling

Fast!

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MADServiceEngineering

41

MAD=“Metric AnomalyDetection”WritteninScala– usingAkkaforconcurrency

UsesnewChunkedExternalCommandfeatureofSplunk6.3– Runsforever,doesn’tgetrestartedevery50kevents– Receivesdatasoonafteritarrivesatanindexer,nopolling

Fast!Designedforgeneral-purposeuse,nocouplingtoITSIruntime

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Howtogetit

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ITSI-AD

ITSI2.3“Batman”(July2016)– ITSIAnomalyDetectionreplacedwithTrendingalgorithm

ITSI2.4“Catwoman”(.conf 2016)– Cohesivealgorithmadded– ComparesentitieswithinaKPI

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Howtogetit

43

ITSI-AD

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Howtogetit

44

ITSI-AD

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Howtogetit

45

ITSI-AD

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THANKYOU


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