ACTIONABLEINSIGHT QoS&QoEKPIsofferanewlevelofincreasinglycriticalvisibilityasnetworksaccelerate

2 ©2015ACCEDIANNETWORKSCompanyConfidential

  “TheEvolutionofInsight”

INFORMATIONISPOWER

  Newlevelsofvisibilityareemergingthatredefinewhatserviceproviderscansee,to…• DetectandisolateQoS&QoEissueswithexceptionalspeed• DiscoverunexpectedrelationshipsandtrendsbetweenKPIs• Uncover‘ghosts’inthenetwork:detect‘invisibile’QoEimpairments• Correlatemetricstoisolaterootcause;amplifywithBigData&Analytics

“Theevolutionofinsight”

Granular,precisevisibilitycombinedwithanalyticsdramaticallyincreasesresolvingpower

  WhyNow?• Networksarebecomingexceptionallycomplex  needbettertools

• Interdependenciesbetweenmetricscausesnewformsofinstability

  needdeeperanalysis

• Shorttermeventsmakinglargerimpact  needfasterdata

• Analytics&BigDatahitmainstream  newlevelsofinsightnowaccessible

DelivertheBestUserExperience

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  Inaccurate,incomplete,

infrequentdataofferslittleinsight.

  Analyticsamplifiesthelimitationsof

poordata.

BUILDINGANACTIONABLEINSIGHTDATASOURCE  “Deepinsightisenabledbyhigh-qualitydata,anddiverseKPIs”

Percentiles

QoEScores

SampleMetrics

Min-Max-Mean&σ

OOO,Lost,LostBurst

KPISignificanceReportedPrecision,

SampleSize

VoIP/VoLTEMOS,R

Addressingoutliers

Define‘Normal’&CatchExceptions

  Perspectives  Statistics&KPIs

AnyFlow

Sub-SecondSampling

  Measurements

  Speed

  Precision

  Focus

µSecResolution

AnyLocation

AnySegment

1-WayMetrics

  Granularity

APrecise,CompleteFoundationforInsight&Analytics

4 ©2015ACCEDIANNETWORKSCompanyConfidential

  OptimizeQoE

  “Instrumentingallkeypointsalongtheservicepath.”

  IdentifyIssues

  LocateSource

Standards-BasedNEPMReflectors

TestPoints

VirtualPMProbe(Actuator)

CSR

Access

AggregationTransport

Core

End-to-End

Virtual&Physical

PMAgent/VNF

NFVI

  TakeAction

VIRTUALIZEDINSTRUMENTATION● COMPLETEVISIBILITY

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EXAMPLE:LOCATION&VoLTEQoE

LosslessRelay

RAN /Backhaul

EPC

IAM_ __LEAVINGWITHOUTYOU!

IAMNOT LEAVINGWITHOUTYOU!

FlowBROKER™RemoteCapture

BrokeredFlows

DecodetheReal-UserExperienceUsingDPI

Simulate&MonitorNetworkQoS&TrendVoLTEQoE

  CentralizedVantagePoint  DistributedVantagePoint  CallCanDegradeAlongtheTransmissionPath

S-GW

Analyzer

6 ©2015ACCEDIANNETWORKSCompanyConfidential

EXAMPLE:LOCATION&VIDEOQoE

HeadEnd

VideoAnalyzer

BasicQoS

SeetheCompletePicture● OptimizeQoE● AccelerateProblemResolution

VideoMOS

VideoPlayback

Central

QoELimitedtohead-endvisibility

CoarseQoSmetricsslowMTTR

Head-EndCapturedFlows

HeadEnd

Real-TimeQoS

Central Access

QoE&KPIs

VideoPlayback VideoPlayback

RealTimeQoS&QoE

CentralizedVideoAnalyzer

Central Remote

VisibilityExtendedtoanyLocation

Real-Time&1-WayMetrics

PerceptiveandNetwork-BasedQoE

End-to-End

SetTop

QoE&KPIs

15min

1sec

vs.

Timeto

Insight“Slow”

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0"

50"

100"

150"

200"

250"

300"

5" 10" 15" 20" 25" 30" 35" 40" 45" 50" 55" 60"

Mbp

s%

Transac+on%Group%Total%

CIR"

Total"5"s"Sampling"

5SEC

MeasureEvery

0"50"100"150"200"250"300"350"400"

1" 3" 5" 7" 9"11"13"15"17"19"21"23"25"27"29"31"33"35"37"39"41"43"45"47"49"51"53"55"57"59"

Mbp

s%

Transac+on%Group%Total%

CIR"

Total"1"s"Sampling"

1SEC

LossMeasureEvery

0"100"200"300"400"500"600"700"

1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"46"49"52"55"58"

Mbp

s%

Transac+on%Group%Total%CIR"

Total"Flowmeter"MIN"

Total"Flowmeter"MAX"

Total"Flowmeter"AVG"

0.1SEC

max

avgmin

MeasureEvery

0"

50"

100"

150"

200"

250"

300"

5" 10" 15" 20" 25" 30" 35" 40" 45" 50" 55" 60"

Mbp

s%Transac+on%Group%Total%

CIR"

Total"5"s"Sampling"

0"

50"

100"

150"

200"

250"

300"

0" 15" 30" 45" 60"

Axis%Title%

Transac.on%Group%Total%

CIR"

Total"15"s"Sampling"

15SEC

LinkCapacity

Utilization

MeasureEvery

ANEWLEVELOFINSIGHT● SAMPLINGSPEED  “EffectsofSamplingFrequency• SameDataSet”

NoProblem

BigProblem

SmallProblem

NoProblem

IncreasingSamplingSpeed&AddingStatisticalPerspectivesChangesEverything

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EXAMPLE:MICROBURSTSHITMOBILEBACKHAUL  “Chattyapplications,inter-cellsignalingcreate‘loss-bursts’thatcanteardowncalls,anddisruptservice”

AverageUtilization

20%

CapacityLimit– anythingbeyond1GbE=lostpackets

“EverythingSeemsFine” Reality: PoorUserExperience

Granular,PreciseMonitoringisCrucialtoDeliveringExceptionalQoE

*theH.264ConstrainedBaselineProfile(CBP)performancetargetsincludeaPLRoflessthan0.1percent– correspondingtoavisibleerrornomorethanonceevery20seconds

Loss>0.1%addsnoticeabledistortiontovideo*

1-msMicro-Bursts:1packetlostoutof1,000=5%LessThroughputplusImpacttoQoE

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250

250

200

200

150

150

100

100

50 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ANEWLEVELOFINSIGHT● PRECISION  “Tier-1NationalNetwork,Asia”

Time(sec)

UEThrou

ghpu

t(Mbp

s)

Delay(m

icrosecond

s)

PacketLossTHROUGHPUT

Precision&SamplingSpeedRevealMulti-MetricCorrelation

VSLATENCY*

*AsMeasuredfromEPCtoeNodeB

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NEWINTERDEPENDENCIESEMERGE“LTE-Advanced,LTE-AdvancedPro,5GIntroduceGreaterNeedtoPreciselyMonitorQoS.”

1

UserE

xperience

OntheLine

15ms

50%

Increased Delay DropInThroughput

2%

80%

Increased Loss DropInThroughput

0%

100%

Throughp

ut

2%1%0.5% 1½%

20%

PacketLoss

0.1%

Source:SKTelecom,LTE-ANetwork(production)Source:“CentralizedSchedulingforJoint-TransmissionCoordinatedMulti-PointinLTE-Advanced”,

S.Brueck,L.Zhao,J.Giese,M.A.Awais,proc.ITG/IEEWorkshoponSmartAntennas,Bremen(WSA'10)

80%↓50%↓40%↓5%↓

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ANALYTICSAMPLIFYINGINSIGHT● CORRELATION

20billion

BigDataRecords

<2seconds

ComputeTime

“Mass”CallFailuresEvery14MinutesreflectedinQoEscore

“NetworkBehavingNormally”KPIswithinthreshold…inisolation

but

ApplyAnalytics Correlateswithrouter

 firmwareversion

Correlations

ActionUpgradeRouter

MOS&PacketLoss• Loss&‘LostBurstMax’

  Lossisoccurringin100msmicro-outages

MOS&MaxDelay: delayspikes

• Likely:RingSwitchIssue(>50ms)

TimetoRootCause

10min

Tier-1MobileNetworkExample

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ANEWLEVELOFINSIGHT● CORRELATEDMETRICS

“Mass”CallFailuresEvery14MinutesreflectedinQoEscore

“Whatlooksinnocentmaybeonepartofabiggerproblem”20:10

20:20

20:30

20:40

20:50

21:00

21:10

21:20

21:30

21:40

21:50

22:00

22:00

22:10

22:20

22:30

VoLTEQoE(MOS)

4

5

3

2

1

0

“NetworkBehavingNormally”

Tier-1MobileNetworkExample

PacketLoss

0

Delay (ms)2502.5%

2.0%

1.5%

1.0%

0.5%

0

200

150

100

50

Threshold Threshold

but

atHighResolution,BothMetricsCorrelateWithFault

PacketLoss

0.20%

0.18%

Delay

0

[ms]

2

4

6

8

5testpacketslostconsecutively:100%lossin100ms– allcallsdropped

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EPC VCXVCX

CSRBackhaulRings

VIRTUALPERFORMANCEASSURANCE&BIGDATAANALYTICSINTEGRATION  “Tier-1Deployment:MaintainImpeccableQoS&QoEatCloud-Scale:100,000’sofeNodeBs,100M’sofmetrics/day”

Standards-BasedNEPMReflectors

PerformancePlatform

DC

Avirtualizedinstrumentationlayer formulti-vendor,multi-technologynetworks

SkyLIGHTDirector

Controller/Probe

BigDataDB(e.g. HDFS)

StreamingQueue

(e.g.Kafka)

InMemory(e.g.Spark)

Visualize

AGR

AggregationLevel3

AGR

AggregationLevel 2Rings

AGR

AGR

AggregationLevel 1Rings

AGR

AGRCSR

Real-TimeAnalytics

Correlate Predict

VCX

Multi-Service SegmentedEnd-to-End

TestPoints

VCX

VCX

EPC

EPC

EPC

…20+times

…Upto15xUpto10x ~5x

 Tier-1LTENetwork:• 20+EPC• Eachserving~5,000

eNodeB• EacheNodeB&CSR

monitored:8.7MPMrecords/day(17.4Mtotal)

• 51KPIs/PMrecord• ~1BKPIs/day/EPC• ~20BKPIs/day

EPC

NationalTransport

SDNController&NFVO,NMS/OSS

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INSIGHTIMPROVINGQoE● ACTIONSFROMANALYTICS  “Tier-1LTEOperator:networkimprovementsresultingfrom1monthofmonitoring”

  ActionstoimprovequalityofLTEbroadband&VoLTE

Type Impairments Action ResultSubscribersAffected

HardwareDefect

Delay,Jitter,Loss ↑ Replace SFP Improvedvoice quality,reducedcall

dropsinaffectedcell 100’s

SystemOverload

Delay,Jitter,Loss↑

Addsmallcellstomacro

ImprovedVoLTE connectivity,callsetuptime&QoE,broadband

throughputandlatency10,000’s

SoftwareDefect

Excessive DelayWithoutAlarm

Replace 10GRouter

Improved throughput inurbanareaservedbymultipleeNodeBsby35% 100,000’s

NetworkCongestion

Loss↑,Throughput↓

Change queuesize(routerCBS) Improvedcarrier aggregation

efficiency:improvedmetroregionthroughputby25%

100,000’sLoss↑ Increasebackhaul

capacity

TopologyDefect Delay>10ms Divide bigring

intosmallerrings 30%+improvementinthroughput 1,000,000’s

FullyVirtualisedEPCwithCloudRAN

VirtualisedInstrumentation

500thousand

1Trillion

NetworkWideAnalytics

DIRECTIMPACT

MoreNetwork

$0 CapEx30 Days

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THEVALUEOFANALYTICS  CorrelatingKPIs

• Acceleratetroubleshootingwithguidedinsight• IdentifycombinationsofvariablesthatdirectlyaffectQoE  uncovernetworkspecificbehavior— sometimescounterintuitive• Buildawatchlistofmostimpactingconditions  reducealarmfatigue:focusonknown‘repeatoffenders’

  PredictiveAnalytics• Trend&interpolatecorrelatedKPIstotriggerproactiveaction• Anticipatefailure:identify‘most-likely’impairments&estimatesubscriberimpact

• Useinsighttofocusonbuildingabetteruserexperience  networkupgrades,optimization,prevention

  MachineLearning• Recordthesuccessrateandresultofanyservicechange,networkreconfigurationorcorrectiveaction

• Learnwhichactionsleadtodesiredresults,overtime• Automatecorrectiveactions,insurewithimmediatefeedback

  “Learnhowyournetworkreallyworks.”

  StrengthofCorrelation

  Time

  PacketLoss

  Time

  Loss&Throughput

  Latency&Throughput

  Latency

  FOCUS

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Correlate Predict

  Analytics   Significance

  Perspectives   DiversityofMetrics:Viewpoints

ACHIEVINGANEWLEVELOFINSIGHT● ASOLIDFOUNDATION

  Deepinsightisn’tpossiblewithbesteffort,‘goodenough’data.

  “InformationisPower”

AnyFlow

Sub-SecondSampling

  Accuracy,Granularity,Recency

  Speed

  Precision

  Focus

µSecResolution

AnyLocation

AnySegment

1-WayMetrics

  Measurements

  Eachincreaseinvisibilityisbuiltontheprevious.

Highresolutionmonitoringformsafoundationforexceptionalinsight.

  “Theintegrationofthebestmetrics,broadstatisticsandreal-timebig-dataanalyticsistheultimatetoolforoperators”

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  “Bringingintelligencetonetworks”

ACCEDIAN’SACTIONABLEINSIGHTARCHITECTURE

VCX

VCXVCX

VCX

  “Fromthemostprecisemetrics,tothemostversatileservicedelivery,toanalyticsintegration”

BigDataDB(e.g. HDFS)

StreamingQueue

(e.g.Flume/Kafka)

InMemoryProcessingVisualize Real-Time

Analytics

Correlate Predict

SDNController&NFVO,NMS/OSS

CollectEnforceCondition

MeasureCreateKPIs

OpenInterfaces

ProviderSystems   CompleteCoverage

• UbiquitousInstrumentationLayer• PhysicalandVirtualEndpoints• Alllayers:QoS&QoE

  SeamlessIntegration• Openinterfacesto…  B/OSS/NMS/SDNControl/NFVO  Big&In-MemoryDataStructures  AnalyticsEngines

  UltimateVisibility• ActionableMetricsinReal-Time• Mostgranular&precise• Segmented&end-to-end

RAMCloud

(e.g.Spark)

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