Pre-Con Ed: Exploiting Operational Intelligence within Database Management tools
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Transcript of Pre-Con Ed: Exploiting Operational Intelligence within Database Management tools
World®’16
ExploitingOperationalIntelligenceWithinDatabaseManagementToolsRonColmone,Sr ConsultingArchitectArunVijayaraghavan,Sr PrincipalProductManagerCATechnologies
MAINFRAMEANDWORKLOADAUTOMATION
MFX76E
2 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
Abstract
Attendthissessiontogetaclose-uplookatcurrentlyembeddedoperationalintelligenceindatabasemanagementutilities.Discoverhowtoexploittheseavailablecapabilitiesaswellasthepossibilitiesforyoutobuildyouown!Youwilllearnhowadvancedautomationrequiresuniqueoperationalintelligencethatcanfurtherevaluatewhichadditionaltaskscanbeautomatedandtowhatdegree.Additionally,learnhowtosimplifyyourdaytodaydecisionmakingtominimizethetimerequiredtoeffectivelymanageandrunanefficientdatabasesystem.Theeffectsofallthis?HowabouthelpingtolowerDB2operatingcostsandimproveuserproductivity,includingmanualeffortandspecifically“specializedorexpert”manualrequirements.
RonColmone
ArunVijayaraghavan
Sr.ConsultingArchitectCATechnologies
Sr.PrincipalProductManagerCATechnologies
3 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
Agenda
USINGDATABASEANALYZERFORAUTOMATINGOBJECTMAINTENANCE
IDENTIFYINGSQLTOANALYZEFORIMPROVINGAPPLICATIONPERFORMANCE
IMPROVINGQUERYPERFORMANCEWITHPLANANALYZER
IDAAACCELERATIONWITHRC/QUERYANDSYSVIEW FORDB2
FUTUREOFOPERATIONALINTELLIGENCEINDB2
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4 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
CADatabaseManagementPortfolioVisionManagingGrowthandComplexitytoAddressEvolvingDataManagementNeeds
Planned Available
IMS DB2
CAAdministrationSuite
CAPerformanceSuite
CAUtilitiesSuite
CARecoverySuite
ExperiencedDBA
WebAppServer
MobileDeveloper
BigDataAnalyst
RESTAPI
NextGenDBA
CAAnalyticsfor
DB2
NextGenDBA
ExternalApps
5 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
DatabaseAnalyzerProvidesOperationalIntelligenceforDB2ObjectMaintenance
6 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
DatabaseAnalyzer– PastandPresent
STATISTICSEVOLUTIONINDB2
QUICKOVERVIEWOFDATABASEANALYZERPROCESSING
TRADITIONALSTATISTICSCOLLECTIONANDANALYSIS
OBJECTMAINTENANCEUSINGDB2REALTIMESTATISTICS(RTS)
PRIORITIZEDOBJECTMAINTENANCE
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7 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
Statisticsevolution– let’sstepbackandremember
§ RUNSTATSSinceDay-1– Collectedstatisticsfortheoptimizer– CollectedstatisticsfortheDBAtodeterminewhenREORGneeded
§ ExtentsaloteasiertotriggerthanCLR,LEAFDIST,FAROFF,NEAROFFetc.
§ Someissues/problemswiththisapproach– DB2cataloggotupdatedwithpotentially“bad”statistics– BIND’sandREBIND’sshouldbepostponeduntilstatisticsfixedby
REORGandanotherRUNSTATS
8 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
Statisticsevolution– let’sstepbackandremember
§ REPORTYESandUPDATENONE– Greatimprovementtoverifyobjectshealthcondition– NoneedtoworryaboutBIND,REBIND,dynamicSQLdueto“bad”
statistics– HadtochangeapplicationstointerpretRUNSTATSSYSOUTasopposed
tousingSQLagainsttheDB2catalogdeterminingREORGetc.
§ Objectskeptgrowinginnumberandsize– RUNSTATSexpensive– Samplingintroducedlateinthegame– Inlinestatscanbeusedwhenloading/reorganizingobject- Limited
9 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
Statisticsevolution– let’sstepbackandremember
§ RTSintroducedinDB2V7– fromDB29mandatory– Newchallengesintroduced(morelater)
§ Manywaystotriggerthereorgofanobject?– Performancemetrics– whenapplicationperformancestarttodegrade– Fixedschedule– Weekly,Monthly,etc.basedonobjectsize/type– Partitionedobjects- EntireTableSpaceoraPartitionatatime?
§ RTSiscommonlyusedtodayforObjectMaintenance
§ ToolslikeDatabaseAnalyzerorDB2’sDSNACCOXSP
10 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
RTSDrivenREORGsandSpaceAdministration
§ Somelimitationstothinkaboutbasedonrequirements– Istheobject(orpartition)over- orunderallocated?– DoyouwanttoalterQTYbasedoncurrentconditions/metrics?– CanyousaveanINDEX-LEVELbydoingareorg
(oneRTScolumnmighthelp)– Doyouneedtoprovideaccuratespacetrending/forecasting– DoyouhaverequirementstogenerateyourownSQLbasedconditions
todrivetheprocess– DoyouhaveaneedforIF-ELSElogicdrivingtheprocessesor
dynamicallydecidehowtheutilityshouldbegenerated– Slidingscalealgorithmresolvesmanyspaceallocationconcerns
11 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
DatabaseAnalyzerProcessOverview
DatabaseAnalyzerExtractandActionProcessing
DB2UtilityJOBs
DatabaseStatisticsExtract
ActionsConditions
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DatabaseAnalyzer– Traditional(OldStyle)Space/Reorg
§ VerycommontobuildanExtractProcedureononeormoreDatabases(couldprocessentiresubsystem)
§ ActionProcedurescanbecreatedorselectedfromlistofbestpracticeDB2maintenanceoptions(Reorg,RUNSTATS,ImageCopy,etc.)
§ ProblemwithTraditionalDatabaseAnalyzerAutomation– Toomanyobjectstoprocess,onlysubsetmayneedattention– CPUintensivecollectingallthestatsneededtodoReorgorAlter– RTShasbeenavailableforsometimeandcanprovidenecessarystats
13 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
DatabaseAnalyzer– PreferredMethodisRTOS
§ RealTimeObjectSelection(RTOS)usesDB2RTS– NewwithR19- ObjectSelectionProfile(OSP)linkedtoExtractprocedure
tofilterobjectstobeprocessed(DatabaseandTablespacemasks)– CangenerateactionJCLforselectedUtilityprocessingbasedonRTS
conditions(Bestpracticearevaluesavailable)– OrcangenerateDatabaseAnalyzerStatisticsonthesubsetofobjectsand
continuetotakeadvantageoftheextensive“traditional”actionconditions– NewwithR19– PrioritizedObjectMaintenance(POM)profilescanbe
linkedtoExtractProcedurestoenabletheActionJCLgeneratedtobeprioritizedbasedonPrioritizedObjectConditions(POC)
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OperationIntelligencewithDetectorSQLCollection
15 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
Detector– SQLCollectionforAcceleratedPerformance
§ AnalyticsAcceleratorUsageScenarios– Rapidlyacceleratebusinesscriticalqueries
§ OftenchallengingtotunequeriesthatalsosupportOLTPworkload– Improveaccesstohistoricaldata– Newbusinessintelligenceusecases
§ UseCADetectortohelpidentifylongrunningqueries– SortQueriesbyHighIN-DB2time– IdentifyDB2Tablesassociatedwiththeselongrunningqueries
§ IfrunningSubsystemAnalyzer,OptionTinDetectorwillshowtablesassociatedwithQuery
16 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
PlanAnalyzertoDeterminePotentialAcceleratedPerformanceImprovements
17 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
PlanAnalyzer- ACCELProfileSupport- ProfileServices
§ CreateanduseACCELprofilesusingtheDB2ProfileServicesfacility.ThefacilityusestheseDB2tables:– SYSIBM.DSN_PROFILE_TABLE– SYSIBM.DSN_PROFILE_HISTORY– SYSIBM.DSN_PROFILE_ATTRIBUTES– SYSIBM.DSN_PROFILE_ATTRIBUTES_HISTORY
§ ACCELprofilecostingisonlyappliedwhenDSNZPARMQUERY_ACCELERATIONisoneoftheseoptions:– ENABLE– ENABLEWITHFALLBACK
18 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
PlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport
ThresholdvaluesdetailstobeusedwhenAcceleratoraccessisconsidered
19.0 ---------- PPA DB2 Profile Services Create --------- 2016/04/05 03:32COMMAND ===>
PROFILEID ===> 5000 FUNCTION ===> ACCEL ENABLED ===> Y REMARKS ===> AIG DEMO >----------------------------------------------------------------- RASST02
ACCELERATOR THRESHOLDS: TABLE CARDINALITY ===> 2000000 N/A | -1 | 1 thru 2,147,483,647 RESULT SIZE ===> 1 N/A | -1 | 1 thru 2,147,483,647 TOTAL COST ===> +.0E+00 N/A | -1 | 0 thru +7.2E+75
19 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
PlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport
STARTPROFoncommandlinewillactivatenewDB2Profile
19.0 -------------- PPA DB2 Command Display ------------- 2016/04/05 03:36COMMAND ===> SCROLL ===> PAGE
LINE 1 OF 24DSNT741I !DB2G DSNT1SDV START PROFILE IS COMPLETED. DSN9022I !DB2G DSNT1STR 'START PROFILE' NORMAL COMPLETION
Profile Services Status Report
Profile ID: 5000 Function: ACCEL Status TS: 2016-04-05-03.36.11.397468 Status : ACCEPTED BY DB2G
Attribute : ACCEL_RESULTSIZE_THRESHOLD Value : 1 Status : ACCEPTED
Attribute : ACCEL_TABLE_THRESHOLD Value : 2,000,000 Status : ACCEPTED
Attribute : ACCEL_TOTALCOST_THRESHOLD Value : 0 Status : ACCEPTED
Profile ID: 8661 Function: ACCEL Status TS: 2016-04-05-03.36.11.397468 Status : REJECTED BY DB2G - DUPLICATED SCOPE SPECIFIED
ID=8661wasalreadyactive.
TogglecommandonProfileServicespanelcanbeusedtoenable/disable
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PlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport
§ PossiblevaluesforQUERYACCELERATIONareobtainedfromSYSACCELERATEDPACKAGESandassignedtothespecialregister– (ENABLEWITHFAILBACK)BehaveslikeENABLE,butifanerroroccursonthefirstOPENof
theacceleratedstaticqueryDB2preventsthequeryfromfailing.Instead,DB2performsatemporarystatement-levelincrementalbindofthequery,andthequeryexecutesinDB2.
– (ELIGIBLE)Indicatesthatstaticqueriesareboundforacceleration,butareacceleratedonlywhentheymeetallaccelerationcriteriawiththeexceptionofcostandheuristicscriteria.QueriesthatdonotmeetthiscriteriaareboundforexecutioninDB2.
– (ALL)Indicatesthatallstaticqueriesareboundforacceleration,androutedtotheDB2Accelerator.IfDB2determinesaquerycannotbeacceleratedandthequeryreferencesauserbasetableorview,thebindorrebindpackagefailswithanerror.
SupportfornewBIND/REBINDcardsforpackages
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CAPlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport
§ EXPLAINwithQUERYACCELERATION– Generatetheaccesspathinformationforpackagesthatwerebound
withthisoption,withoutissuingaREBIND.– FUTUREexplainwillSETthespecialregisterCURRENTQUERY
ACCELERATIONbeforethedynamicexplaintoreflectthevalueboundinthecatalogforthepackage.
– IfDSNZPARMACCELMODEL=YES,FutureExplainoverridesthevaluethatisspecifiedintheQUERYACCELERATIONbindoptiontoNONE.Thisactionallowstheaccesspathtogeneratesuccessfully.(nextslidewillillustrate)
22 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
CAPlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport
ACCESStypeillustratesthequerycanbeexecutedontheIBMDB2Analytics
Accelerator
ViewDSN_QUERYINFO_TABLEwhenusingExplainProfile– PLAN_TABLEOption===>ALL
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AccelerateandMonitorIDAATableswithRC/Query,BatchProcessorandSysview forDB2
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CADatabaseAdministrationforDB2forz/OS
§ CARC/Query &BatchProcessor(canusewithautomation)– Noneedtonavigatebetweenmultiplesolutions– ManageDB2AnalyticsAcceleratorwithinyourCADB2solutions
§ Display/Start/StopAccelerator§ Add/RemovetablestoDB2AnalyticsAccelerator§ Loadtabletotheaccelerator§ Enable/Disabletableacceleration/replication§ AlterAcceleratedtableDist.&OrgKeys§ ArchiveAcceleratedtable§ ForceRemoveTablefromDB2AnalyticsAccelerator§ Restorethearchivedmovedpartitionsdata
NewwithR19
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ManageDB2AnalyticsAcceleratorUseCARC/QueryforIDAAadministration
Use“?”linecommandtoseeavailablecommands.
IDAATablelevelfunctionsareavailabletoDefine,Alter,Remove,Load,etc.
26 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
ManageDB2AnalyticsAccelerator
§ LoadDB2TableintoIDAA– RC/QALOADCommand
– CanloadrangeofpartitionsorentireTable
– UsesIDAALoaderutility
– MostcommandssuchasALOADareavailableusingBatchProcessorScripts
UseCARC/QueryforIDAAadministration
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SYSVIEWforDB2– IDAASubsystemStatistics
§ Newacceleratorrelatedfieldswereaddedtorecords:– Databasestatistics(IFCIDs2and1002)– DB2systemparameters(IFCIDs106and1006)
§ Updatedreports:– SYSACCEL/SYSACDTL:AcceleratorList/Detail– HSACCLST/SYSCCDTL:AcceleratorServerList/Detail(history)– HSUACLST/HSUACDTL:AcceleratorServerList/DetailSummary(history)– BTSTATR1:StatisticsDataTrace(batch)– BTSTASM2:SummaryofDB2databaseaddressspacestatistics(batch)– SYSSTATS/SYSTATA,GRPSTATS/GRPSTATA:SystemOverview
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DB2AnalyticsAccelerator
§ Subsystemstatistics:AcceleratorDetails– SYSACDTL:
AcceleratorDetails– HSACCDTL/
HSUACDTL:AcceleratorServerDetails/Summary
CASYSVIEW®PerformanceManagementforDB2
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DataDrivenDBPerformanceManagementMainframeOperationalIntelligence
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EXPERTSYSTEMS&MACHINELEARNING§ AnalysisofHistorical,Near
Real-timeplusLivedata§ DataMining,MachineLearning
&Autonomics§ SelfDrivingCars
DATAMINING&DECISIONSUPPORT§ AnalysisofHistorical&NearReal-timeData§ DataMining,Aggregations&Decision
Support§ NavigationToolswithTrafficUpdates
BI&STATISTICALANALYSIS§ AnalysisofHistoricalData§ BIReporting,Slicing/Dicing§ BasicNavigationTools
EvolutionofOperationalIntelligenceActionableanalyticswithsignificantadvancestowardszerotouchoperations
ANALYTICS1.0BUSINESSINTELLIGENCE
ANALYTICS3.0DATA-DRIVENAPPLICATIONS
ANALYTICS2.0BIGDATA
TechnologyTrend
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55%ofapps
dependonmainframe
70%world’scorporate
dataisonamainframe
70%oftransactionsflowthroughmainframe
64%Increaseinmainframeworkloads
ANOMALYDETECTION
BUSINESSSERVICEMANAGEMENT
EXPERTSYSTEMS
SECURITYBREACH
DETECTION
IndustryOpportunityDataAnalyticsandtheMainframeOpportunity
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MainframeOperationalIntelligenceLeveragetechnologytrendtosolveanimportantMainframeproblem
ANOMALYDETECTION
REPORTINGMODERNIZATION
BUSINESSSERVICEPERSPECTIVES
EXPERTSYSTEMS
• Reacttothresholdevents
• Selfserviceviews• ModernU/X
• Predictanomalies• Proactiveresponse
• Topologydiscovery• Businessserviceperspectives
• Predictbusinessservicedisruption
• Rootcausehypothesis• Resolution guidance• Automateresponse
DataAnalyticsBI/StatisticalModeling
USERS
Generalist
MAINFRAM
EOPERA
TIONS
Experts
Specialists
Automation
ENTERP
RISE
SUPP
ORT
MTTR&FirefightingOptimizedPerformance&Efficiency
AutomationDataDrivenOperations
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IntelligentMainframeOperations- LiftingtheBurden
DATA-DRIVENAPPLICATIONS
ANALYTICALTOOLS&SKILLS
IN-HOUSEDATASCIENCE
IN-HOUSEDOMAINEXPERTISE
+
+
YOURBURDEN MainframeTeamCenter
LettheDATAdotheWORKforyou
DoMOREwithLESS!
Fueledby:§ advancedanalytic
algorithms§ machinelearning
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MainframeOperationalIntelligenceKeycapabilitiesbeingdevelopedtomanagethelifecycleofyourdataassets
AnalyticsProcessing Multi-ChannelDelivery
Multi-ChannelDataCapture
DistributedDB2,Oracle,MySQL,
Cassandra...
MainframeVSAM,DB2,IMSDB,IDMS,DATACOM,
SMF,Syslogs,Vtape,CICS
Itsyourdata,leverageittodotheworkforyou!
DataLifecycleManagement
• ReportDistribution• AnomalyAlerts• Ad-HocAnalysisTools• APIbaseddataaccess• Automationframeworks
ExpertSystemsAutonomics
Transport&Integration ScalableData
Storage
DataScienceandAnomalyDetection
BusinessIntelligence
ITOperationsIntelligence
APIManagement
MasterDataManagement
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DataDrivenDBPerformanceManagementProblem,OpportunityandProposedSolution
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INCREASINGCOMPLEXITY REACTIVETOPROACTIVE EARLYWARNING
CompaniesaredevelopingcomplexSQL applicationsonDB2.Performanceoftendegradesovertimewithoutanyonenoticing(thecreepingtrend).
AnintelligentsystemthatcanrecognizeandprioritizesignificantchangesinSQLperformancebeforeitstartstoimpactresourceoverheadsand servicelevelagreements
ADBAoftendoesnotrecognizedegradationuntilcustomerscomplain orservicelevelagreementshavebeenmissed.
TheSituationEverincreasingapplicationvolumesandcomplexity
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§ Howdoyoudeterminewhenperformancestartedtodegrade?§ HowlongdoesittaketoidentifytheproblematicSQLstatements?§ Doyouknowhowthestatementsexecutedbeforetheperformance
degraded?§ Doyouhavealogofapplicationperformanceproblemsand
resolutions?– Canyoucomparethislogwithcurrentproblems?
§ ManycustomersoffloadCADetectordatastoreintoDB2tables– Queryheaviestplans/packages&manuallycompareto“baselines”– Noefficientmethodtore-evaluatebaselineswhen“theworldchanges”
– Howtomonitor“standarddeviation”and“creepingtrend”iscomplexandcumbersome
TheChallengeHowdoyouresolveperformanceproblems?
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Capture CADetectorcollectsSQL
intervalperformancemetricsforanalysis.Createsan historicalviewofapplicationperformance,whichwillbestoredinthecloud.
Baselin
e BaselinesarecreatedthatcontainastatisticalanalysisofapplicationSQLperformanceforachosen,representative,period-in-time.Baselinerepositorycanbeupdatedwhenbaselinesaredeterminedtonolongerberepresentative.
Analyze Analyzesapplication
performancedeviationsfromnormalexecutionbehavior(i.e.,deviationsfrombaselines)andlogsinaneventsrepository. N
otify
DuringtheAnalyzestep,notificationsofperformancedeviationscanbesenttokeypersonnelviaemail.
Review
InadditiontooutputfromCapture,Baseline,andAnalyzetheapplicationwillallowyoutoreviewapplicationperformancehistorythroughawebbrowserinterface.Deeperanalyticsandvisualizationareavailablethroughthebrowserdashboard.
Mainframe
TheSolutionMainframeTeamCenter–OperationalIntelligence
39 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
USECASE:Sherman,Debbie,andFredhavedeepexperienceandskillsintheirrespectiveareas.Theyhatebeingcalledafterasystemproblemhasoccurredandbeingaskedtoprovethattheirareawasatfaultorinnocent.Theywouldlikeasystemthattellsthemwhentheirareaisbehavingabnormally,sothattheycanaddressitbeforeitcomestotheattentionoftheSystemsPerformanceEngineer.
• Operations,SystemsProgrammer,• NetworkEngineer,ApplicationDBA
PERSONA:
PROBLEM§ “Avoid,detectandpredictissuesthatmightbeaproblem”§ “Thresholdsarehardtomaintainandgeneratelargeamountoffalsepositives”§ “Abilitytoseemultipleviewsofdata”§ “Abilitytocreateviewsofinformationfaster”§ “ThecurrentU/Xpreventseasycollaborationandaccesstoanalytics”
AnomalyDetection– ProblemAvoidance/Prediction
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AnomalyDetection– ProblemAvoidance/PredictionSOLUTION§ Detectanomalies&predictissuesreal-time,alertbasedonpredefinedrules
§ Leveragehistoricaldataandmachinelearningfordynamicthresholds
§ SimplifiedU/X– browseraccess&designedforcollaboration
BOTTOMLINE§ HighAvailability
§ Problemavoidance§ ReducedMTTR
§ ReduceSMEdependenceforissuedetection
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AnomalyDetection– DeepDive
§ UtilizeHistoricaldata§ DefinebandsofLikelyandunlikelyvalues
§ Maprealtimemetricstreamsagainstsystemdefinednormal
§ Multi-pointalertsgeneratedusingindustry-standardWestern-Electricrules
§ Makestaticthresholdsoptional!
Letdatadotheworkforyou
Unlikely
MostLikely
Metric
Time
TypicalVolatility
Anomaly
Tasksready
tobedispatched
LessLikely
42 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD
USE CASE:As someone owning IT operations and responsible for technology progress, my MF systems should exploitplatforms like Linux (X86 & Z) to offload data processing and non essential management capabilities. Lack of thiscapability limits the ability to extend the strengths of the mainframe with next generation technology that issupported on Linux.
• BusinessOwner• TechnologyOwner
PERSONA:
PROBLEM§ “Helpmedeployyouranalyticssolutioninhoursnotdays”§ “Nosignificantincreaseonmyteamsworkload,wearenotlinux experts”§ “WeplantoexpandourzecosystemtoincludeLinuxandrequiretoolstoleverageourstrategy”§ “Ensuringtechnologyprogresswithoutlosingthereliability/securityofMFishighvaluegoal”§ ”Ensureanalyticssolutiondonotincreasemyoperationscostssignificantly”
SimplifiedDeployment– MinimizeCostandRisk
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SimplifiedDeployment– MinimizeCostandRisk
Web-basedUI
RestAPI
z/OS
PerformanceManagement
DatabaseManagement
RestAPIs
StorageManagement
SoftwareVirtual
Appliance
BENEFITS§ LowCostcomputing§ Non-Invasive§ RetainsSecurity§ SetupinMinutes
ANALYTICSSTREAM
EXISTINGPRODUCTS
Browser
SOLUTION§ Virtualappliance§ ContainerizedPlug-n-play§ SimpleConfig GUI
MetricBaseline
AnomalyDetection
Correlations
Aggregation
HistoricalData
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BusinessServicePerspectives&TopologyViewsSOLUTION§ Monitorservicelevelmetrics
§ Analyzemetricswithabilitytodrilldownacrossentiretopology
§ Trackandprioritizeeventsaffectingservices,consolidatealerts
§ Correlatemetricsviamachinelearning
§ QuickrootcauseanalysisandreducedMTTR
BOTTOMLINE§ ReducedMTTR
§ ReduceSMEdependenceforissuetriage/investigation
§ Improvedenduserexperience
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ExpertSystems– IntelligentMainframe
SME
•SOLUTION§ AssistedtriagethathelpsfasterRCA,reduced
MTTRandfacilitatesbetterdecisions§ Utilizecorrelationdatasciencetofindclusters
ofalertsandrelationships
§ Leveragemachinelearning&yourdatatothesystemtriageandisolatetheproblem
§ Systemlearnsfromexperts,helpsonboardjuniorstafffaster
BENEFITS§ ReducedMTTR
§ Higherproductivityofjuniorstaff§ TransferofTribalknowledgetoanexpertsystem
§ Lowercostsofoperations,noincreaseinrisks
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LEVERAGEDATAITSYOURASSET
MOVEFROMREACTIVETOPROACTIVE
DETECTANOMALIESAVOIDPROBLEMS
WrapUpandKeyTakeawayMainframeOperationalIntelligence&DB2Tools
Expertuserswithdecadesofexperience
Highlycustomizable,flexibletoolkits
Basicsoftwareintelligence
InexperiencedusersnewtoMainframe
Toolsthatguideuserstodecisions
Predictivesoftware
intelligence
WeInviteyoutoparticipateinDesign@CAandTechPreview!
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MainframeandWorkloadAutomation
FormoreinformationonMainframeandWorkloadAutomation,pleasevisit:http://cainc.to/9GQ2JI
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