Passive realtimedatacenter fault detection and localization · Load balanced traffic simplifies...

Passiverealtime datacenterfaultdetectionandlocalization

ArjunRoy,JamesHongyi Zeng*,JasmeetBagga*,andAlexC.SnoerenUniversityofCalifornia,SanDiegoFacebook*

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“Itwouldbeniceifwecouldfigureoutwhichlinkwascausingtheseretransmits.”

- Ranjeeth Dasineni,Facebook(paraphrased)

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Contemporarydatacenternetwork

However:faultsmaybepartial/intermittent.3

Partialfaults:Afewexamples

• Netpilot (Sigcomm 2011):Framecheckerror,unequalECMPhashing,etc.Wu,Xin,etal."Netpilot:automatingdatacenternetworkfailuremitigation." ACMSIGCOMMComputerCommunicationReview 42.4(2012):419-430.

• Everflow (Sigcomm 2015):TCAMbiterrors,silentpacketdrops.Zhu,Yibo,etal."Packet-LevelTelemetryinLargeDatacenterNetworks.”SIGCOMM,2015.

• Pingmesh (Sigcomm 2015):“fiberFCS…errors,switchingASICdefects,switchfabricflaw,switchsoftwarebug,NICconfigurationissue,networkcongestions,etc.Wehaveseenallthesetypesofissuesinourproductionnetworks.”

Guo,Chuanxiong,etal."Pingmesh:ALarge-ScaleSystemforDataCenterNetworkLatencyMeasurementandAnalysis.” SIGCOMM,2015.

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Vastbodyofpriorwork(justasmallsample…)• Applicationinstrumentation:variousproductionsystems

• Activeprobing:Pingmesh (SIGCOMM’15),NetNorad (Facebook),ATPG(CoNEXT ‘12),Everflow (SIGCOMM‘15)

• Machinelearning:NetPoirot (SIGCOMM’16)

• Graphalgorithms:Gestalt(Usenix ATC‘14),SCORE(NSDI‘05)

• Pathtracing: Everflow (SIGCOMM‘15),NetNorad (Facebook),NetSight (NSDI‘14),TinyPacketPrograms(SIGCOMM‘14)

• Networkinstrumentation:FlowRadar (NSDI’16),Planck(SIGCOMM‘14),NetPilot (SIGCOMM‘11)

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Weexploit:highlyregularloadbalancedtraffic

Sourceracktrafficmagnitude

Destinationracktrafficmagnitude

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ArjunRoy,Hongyi Zeng,JasmeetBagga,GeorgePorter,andAlexC.Snoeren.InsidetheSocialNetwork's(Datacenter)Network. ACMSIGCOMM'15,London,England.

Loadbalancedtrafficsimplifiesfaulthandling

• Evenlyloadedpathsmeansperpathperformanceissimilarifnoerrors.• Networkfaultsleadtooutlierpaths.• Ifflownetworkpathknown,cancorrelateflowperformancewithpath.

• Approachallowsustofindandlocalizefaults:• Inanapplicationagnosticmanner• Incurringnoadditionalprobingoverhead• Morerapidlythanpriorpublishedworks

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Facebookdatacentertopology

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AlexeyAndreyev.Introducingdatacenterfabric,thenext-generationFacebookdatacenternetwork.https://code.facebook.com/posts/360346274145943/introducing-data-center-fabric-the-next-generation-facebook-data-center-network/

FindingpathinformationatFacebook

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Solution:aggregationswitchmarkspacketsbasedoncoredownlinktraversed.

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Howdoweusepathinformation?

• Inprinciple:cancompareflowperformancebypath.1. Combinatorialdisaster:O(10,000)pathsfromsinglehosttoremoteracks.2. Nolocalization:doesn’ttelluswhichlink/switchisatfault.

• But:forthistrafficpattern,ECMProutinggivesusevenbytes/link.

• Solution:Justcomparelinks!

Create“EquivalenceSets”:setsoflinkshandlingsimilarload

andexhibitingsimilarperformance,intheabsenceoffaults

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Equivalencesets:1. Reducesnumberofcomparisonsneeded.

2. Pinpointsfaulttospecificlocation.

EquivalencesetsinFacebooktopologyCoreCoreCore

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Equivalenceset:4uplinksfromeachToR

topodAgg layer

…eachhasclosetoidenticalperformancedistribution

inabsenceoferrors19

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…eachhasclosetoidenticalperformancedistribution

inabsenceoferrors

Equivalenceset:NuplinksfrompodAgg layertocorelayer

EquivalencesetsinFacebooktopology

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Outlieranalysiswithapplicationagnosticmetrics

Hostsalreadytrackmetricsforcongestioncontrolorperformancemonitoring:

TCPCongestionwindow:Affectedbypacketloss.TCPRetransmits:Affectedbypacketloss.SmoothedRoundtriptime:Affectedbylatencyspikes.Systemcalllatency: Affectedbypacketloss.

Caveat:Canbedifficulttodetermineifanaffectisduetoafaultylink,overloadedhosts,applicationvariance,etc.

Withequivalencesetbasedgrouping,wecancomparedistributionsbylink.

Onlylinkfaultscausevariancebetweenlinks.

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DemonstratingequivalencesetsfromAgg toToR

(1)ToR markspacketDSCP

perinboundlink

(2)HostaggregatesTCPmetricsbylink(3b)Host drops0.5%ofpacketstraversinglink

(3a)Wesimulateerroronthislink:

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Host ToRAgg 2

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TCPCongestionwindowinAgg toToR equivalenceset

Cacheserver 23

Congestionwindowsignalisapplicationagnostic

Cacheserver Webserver24

Weuse:TCPretransmitsinourwork

Cacheserver Webserver25

Detectingfaultsinproduction

• Monitoredtrafficthroughpodaggregationswitch.1. Nofaultsinjected.2. CollectedTCPmetricdataon30webserverhosts.3. Equivalenceset:fourlinecards connectingtocorelayer

(eachlinecard hasequalshareofuplinks).

• OnJanuary25th,asinglelinecard hadasoftwarefault.1. Linecard controllersoftwarehung.2. BGProutestimedout,productiontrafficthroughlinecard routedaway.3. Afewminuteslater,NetNORAD flaggedunresponsivelinecard.

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Faultvisibletoourapproachin30seconds

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Classifyingfaultylinks

• “Doesthislinkhavemoreretransmitsperflowthantheotherlinks?”

• “Dotwodistributionshavethesamemean,orisonegreater?”

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Classifier:compareeachlinktootherlinkswithonesampleStudent’sT-Test.

OnlinefaultmonitoringwithT-Testalone

• Inprinciple:cansetupasystemthatusesendhostT-Testresulttotelluswhichnetworklinksarefaulty.

• However:byitselfthisissusceptibletoFalsePositives.

• Can’taffordfalsepositivesinnetworkwithO(10,000)links!

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Accountingforfalsepositives

• However,twocharacteristicsaidus:1. Per-hostfalsepositivesevenlydistributedperlinkovertime.2. Datacenterhasaplethoraofhostsforwhichthisistrue.

• Thus,we’renottryingtoseeif agivenlinkismarkedfaultybyhosts.

• Instead,weonceagainperformoutlieranalysis.1. “Areallthelinksbeingmarkedfaultybyhostsatsimilarrates?”2. “Arehostsflaggingaparticularsubsetoflinksasfaultyathigherrates?”

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Chi-squaredtest:determinesifanylinksareoutliers.

P-Value≈ 1:“Yes,allthelinksbeingmarkedfaultybyhostsatsimilarrates.”

P-Value≈ 0: “No,asubsethasacomparativelyhighpercentageofhostsclaimingfault.”

Evaluationinthedatacenter

• Smalldetectionsurface;didnotdetectany‘organic’partialfaults.

• Approach:inject‘simulated’faultstoevaluateapproach.

• Inducedavarietyoffaultscenariostochallengeoursystem.

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Evaluationinthedatacenter:faultscenarios

• Minisculefaults:faultscanhaveverylowdroprates.

• Concurrentfaults:multiplefaultscanoccursimultaneously.

• Maskedfaults:largerfaultcanmaskeffectofminisculefault.

• Correlatedfaults:hardwarefaultcanimpactmultiplenearbylinks,confoundingoutlieranalysis.

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Evaluationinthedatacenter:faultscenarios

• Minisculefaults:faultscanhavevery lowdroprates.

• Concurrentfaults:multiplefaultscanoccursimultaneously.

• Maskedfaults:largerfaultcanmaskeffectofminisculefault.

• Correlatedfaults:hardwarefaultcanimpactmultiplenearbylinks,confoundingoutlieranalysis.

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Findingminisculefaults:experimentsetup

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Equivalenceset:NuplinksfrompodAgg layertocorelayer

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Partialfaultinducedonsingle

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Faultdetectionratevsdroprate

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Minisculefaults:choosingbetweendetectionspeedandsensitivity

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“Itwouldbeniceifwecouldfigureoutwhichlinkwascausingtheseretransmits.”

Ranjeeth Dasineni,Facebook(paraphrased)

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InterpretingtheT-Test

1. T-Statistic:“Doesthislinkhavemoreorlessretransmitsthanaverage?”

• Positive T-statisticmeanslargerthanaverage.• Negative T-statisticmeanssmallerthanaverage.

2. P-Value:“Isthedifferenceinmeanbigenoughtoconcernus?”

• Closeto0meansthislinkcouldbeanoutlier.• Closeto1meanswearenotconcerned.

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InterpretingtheT-Test

P-value0,t-stat>0

P-value1,t-stat≈0

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Passive realtimedatacenter fault detection and localization · Load balanced traffic simplifies...

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