pre-con ed: operational intelligence – monitoring the past to better understand today

World®’16

MainframeOperationalIntelligence– MonitoringthePasttoBetterUnderstandTodayDavidMyers– SeniorPrincipalProductManager–IntelligentMainframe– CATechnologies

MFX145E

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Abstract

JoinustolearnhowthenewreleaseofCASYSVIEWmakesiteasiertoaccessandanalyzehistoricalperformancedatatobetterunderstandcurrentissuesandsourceofproblems.

DavidMyers

CATechnologiesSr.PrincipalProductManager


Agenda

HISTORICALANALYSIS

GREEN-HIGHWAYS:LEARNINGALGORITHMSANDANALYSIS

1

2


MainframeTeamCenter– OperationalIntelligenceDetectabnormalpatternsofoperation,soyoucantakeactionbeforethefirestarts

KeyFeatures§ DataStreamingAPIs§ HistoricalDataWarehouse§ HistoricalAnalysistools§ DynamicBaseliningandThresholdManagement

§ AnomalyDetection§ AlarmManagementandNotifications

§ Appliance-BasedInstall


HistoricalAnalysis

Analysiswindowexpansion/compression

TimeTravelMulti-metricvisualization

ToolsPane

HistoricalOverview


DEMO


SoWhat’s“Normal”?


GoalsforPredictiveAnalytics

1. Dynamicallyandautomaticallydeterminewhatistypicaloperationalstateforagivensystem

2. Generatealertsforabnormalscenarios,whilereducingfalse-alarmsforknownvarianceinbehavior– Businesscycles(e.g.,End-of-Quarterprocessing,BlackFriday,

Statementgeneration,etc.)– Naturalvolatility


FromHistoricalDataToQuantitativePrediction

Ifwewishtoknowifametricisactingtypically,wecancomparepresentvaluestopreviousvalues

– Provideshistoricalcontext


FromHistoricalDataToQuantitativePrediction

Takingthisfurther,wecanusehistoricalvaluestomakeapredictionaboutwhatrange ametricwilltakeawhattime.

– Notjustifitisunusual,butquantitatively howunusualitis.


PredictiveAnalyticswithOperationalIntelligence

Components:

§ Waveletdecomposition

§ KernelDensityEstimator(KDE)

§ Exponentially-WeightedMoving-Average(EWMA)

HistoricalInputData,

SameStageofBusinessCycle

WaveletDecomposition

Exponentially-WeightedMoving-Average

KernelDensityEstimator

PredictiveModel

0,1,2,3,4,,5,7,8,

9,0123,14,4

0,1,2,3,4,,5,7,8,

9,0123,14,4

0,1,2,3,4,,5,7,8,

9,0123,14,4


FromPredictionsToAlerts

§ UtilizeHistoricaldata§ DefinebandsofLikelyandunlikelyvalues

§ Maprealtimemetricstreamsagainstsystemdefinednormal

§ Multi-pointalertsgeneratedusingindustry-standardWestern-Electricrules

§ Makestaticthresholdsoptional!

Unlikely

MostLikely

Metric

Time

TypicalVolatility

Anomaly

Tasksreadyto

bedispatched

LessLikely


PredictionIntervalsvsMeanandStandardDeviation

KDE MeanandStandarddeviationForanydistribution,includingthosewhichhave multiplepeaks,or are asymmetric

Presumessymmetricbell-curve distribution

Sizeofthe+/- bandsmay beequalorunequalbasedon data

All+/- bands areofequalwidth

Higher computationtime/storage Lowercomputationtime/storage

Robustagainstrare,extremevalues Distortedbyrare, extremevalues


DifferentialAnalysis

PredictionBands

AnomalyStates

Detaildrill-down

Metrictracker

GreenHighway VisualizationToggle


DEMO


Questions?


MustSeeDemos

IntelligentMainframe:

AdvancedAnalytics

CADesignVisionforIntelligentMainframeOptimizingMainframeOperations


End-To-EndPerformance


Automation


SmartBarIntelligentMainframeandAdvancedAnalytics

TalktoAnalyticsExpertsatMainframeSmartBar


RecommendedSessions

SESSION# TITLE DATE/TIME

MFT158S IntelligentMainframeManagement&theDriveTowardsNo-TouchAutomation

11/16/2016at12:45pm

MFT167T MainframeTeamCenter– EventManagementandAutomationSneakPeek 11/16/2016at3:45pm

MFX161S IntelligentMainframeManagement:TheEvolutionofExpertSystems 11/16/2016at4:30pm

MFT169T GettheEntirePicture:End-to-EndInfrastructure&AppMonitoring 11/17/2016at11:30am

MFX162S CrackOpentheBigBlackBoxforFasterProblemRemediation 11/17/2016at12:45pm

MFX163S ModernSystemIntelligence:MakingtheDatadotheWork 11/17/2016at1:45pm

MFT159P Panel:WorkingSmarterNotHarderwithEmbeddedMainframeIntelligence 11/17/2016at4:30pm


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Thankyou.

pre-con ed: operational intelligence – monitoring the past to better understand today

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