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Intelligent Mainframe Management with CA

For Informational Purposes Only Terms of this Presentation

© 2016 CA. All rights reserved. All trademarks referenced herein belong to their respective companies. The presentation provided at New England DB2 Users Group is intended for information purposes only and does not form any type of warranty. Some of the specific slides with customer references relate to customer's specific use and experience of CA products and solutions so actual results may vary.

Certain information in this presentation may outline CA’s general product direction. This presentation shall not serve to (i) affect the rights and/or obligations of CA or its licensees under any existing or future license agreement or services agreement relating to any CA software product; or (ii) amend any product documentation or specifications for any CA software product. This presentation is based on current information and resource allocations as of September 1, 2016, and is subject to change or withdrawal by CA at any time without notice. The development, release and timing of any features or functionality described in this presentation remain at CA’s sole discretion.

Notwithstanding anything in this presentation to the contrary, upon the general availability of any future CA product release referenced in this presentation, CA may make such release available to new licensees in the form of a regularly scheduled major product release. Such release may be made available to licensees of the product who are active subscribers to CA maintenance and support, on a when and if-available basis. The information in this presentation is not deemed to be incorporated into any contract.

© 2016 CA. All rights reserved. CA confidential and proprietary information. No unauthorized use, copying or distribution

Agenda

ROLE OF MAINFRAME IN THE MODERN APP ECONOMY

WHAT ARE OUR CUSTOMERS PRIORITIES?

WHAT IS CA DOING TO ADDRESS THOSE PRIORITIES

PREVIEW NEXT VERSION OF SYSTEMS MANAGEMENT

REVIEW DESIGN THINKING PROCESS, USE CASES

DEMO AND DEEP DIVE

Mainframe in the modern app economy

55%of apps

depend on mainframe

70%world’s

corporate data is on a

mainframe

70% of

transactions flow through mainframe

64%increase in mainframe workloads

ANOMALY DETECTION

BUSINESS SERVICE MANAGEMENT

EXPERT SYSTEMS

SECURITY BREACH

DETECTION


Our Goal - Building the Intelligent Mainframe

EXPERT TOOLS

Monitor Status, react to threshold based

events

Discover & monitorbusiness service

topology

Root cause

hypothesis

Automate event response

Automation

SPECIALIST TOOLS

Predict and detect anomalies, respond to

anomaly alerts

Data Driven Operations

Data AnalyticsBI/Statistical Modeling

GENERALIST TOOLS

Focused

resolution

guidance

USER PERSONA

Generalist

MA

INFR

AM

EO

PER

ATI

ON

S

Experts

Specialists

Automation

ENTE

RP

RIS

E SU

PP

OR

T

CostANOMALY DETECTION

BUSINESS SERVICE MANAGEMENT

SECURITY BREACH DETECTION

EXPERT SYSTEMS

REPORTING

Predict business service performance


Progress To-date – Where does the transition start?

SOLUTIONS

Database Management– Expands value of CA Detector

– Currently in Validation

Performance Management– Expands value of CA SYSVIEW

– Validation in June

– GA ~ Fall 2016

Makes existing solutions moreintelligent

Focus on problem avoidance viaanomaly detection & dynamicthresholds

Simplified U/X designed for quickand easy collaboration

BENEFITS

Register to participate in validation at:

validate.ca.com


Use CasesProblem, Opportunity and Proposed Solution

Mainframe IT Ops Team Persona

SHERMANSystems Engineer

RALPHSystems Performance Analyst

MY PAINMonitoring many systems & Devices

HELP MESimplify alerts, meaning

and action

MY PAINBottleneck – work on

all issues

HELP MEUnderstand app and system

performance characteristics quickly

MY PAINFirefighting and identifyinglikely sources of future fires

HELP MEDetect anomalies before they become an issue

and collaboration to isolate the functional area

DEBBIENetwork Engineer

PETE Level 1 Support Analyst

Cross Enterprise

FREDApplication DBA


Anomaly Detection – Problem AvoidanceUse Case & Solution Concept

USE CASE:Sherman, Debbie, and Fred have deep experience and skills in their respective areas. They hate being called after a system problem has occurred and being asked to prove that their area was at fault or innocent. They would like a system that tells them when their area is behaving abnormally, so that they can address it before it comes to the attention of the Systems Performance Engineer.

• Systems Programmer,• Network Engineer,• Application DBA

PERSONA:

PROBLEM

“Avoid, detect and predict issues that might be a problem”

“Thresholds are hard to maintain and generate large amount of false positives”

“Ability to see multiple views of data ”

“Ability to create views of information faster”

“The current U/X prevents easy collaboration and access to analytics”© 2016 CA. All rights reserved. CA confidential and proprietary information. No unauthorized use, copying or distribution

Modern tools & views – Problem Avoidance/RemediationUse Case & Solution Concept

SOLUTION

Dashboards on demand and rapidactions

Leverage UX that complements SMEexisting workflow

Simplified U/X – browser access &designed for collaboration

BOTTOM LINE

High Availability

Problem avoidance

Reduced MTTR

Reduce SME dependence for issuedetection


Anomaly Detection– Problem Avoidance/PredictionUse Case & Solution Concept

SOLUTION

Detect anomalies & predict issues real-time, alert based on predefined rules

Leverage historical data and machinelearning for dynamic thresholds

Simplified U/X – browser access &designed for collaboration

BOTTOM LINE

High Availability

Problem avoidance

Reduced MTTR

Reduce SME dependence for issuedetection


Magic Behind the Analytics – Data Science and Prediction

Historical

Input Data,

Same Stage of

Business Cycle

Wavelet

Decomposition

Exponentially-

Weighted

Moving-

Average

Kernel Density

Estimator

Predictive Model

Goals:– Dynamically and automatically

determine baselines and

thresholds

– Generate alerts for abnormal

scenarios, eliminate false

positives and minimize false

negatives

Business cycles

Natural volatility


Solution Demo

Today’s Challenge

Companies are developing complex SQL applications on DB2

– Time is over where we (the DBA’s) knew the SQL transactions – dynamic SQL taking is over !

– Performance often degrades over time without anyone noticing (the creeping trend)

– The degradation can happen slowly (over weeks or months) or quickly (over hours or days)

When performance degrades, DBAs have steps they can take to “restore”performance

– But with thousands to hundreds of thousands of SQL statements executing, a DBA often does notrecognize degradation until customers complain or service level agreements have been missed

DBAs need an early warning system – Predictive <> Reactive

– To recognize and prioritize where and when significant changes in SQL performance are occurring,before customers complain or service level agreements are missed

– Intelligent dynamic baseline metrics to avoid false positives / too many false negatives.

– The system needs to be intelligent, the interface needs to be intuitive, and customers cannot affordadditional overhead on their z/OS systems


Today - DBA struggles to identify what to tuneTo minimize elapsed time, CPU, and Getpages - while maintaining SLAs

Methods Adjusts SQL statements, runs reorg utility, tunes databases, etc.

Challenge Finding best applications to tune – Detector really helps !!

Data CA Detector interval performance metrics for executions of SQL statements, packages, and plans

Limitations CA Detector does a great job telling you where you are• How you got here (is today’s behavior “normal”?)• Where you are going (is performance getting better or worse?)• Where the greatest tuning potential is (which applications

account for the greatest increases in resource usage ? )• Does is matter if a transaction is using 15 GETP as opposed to 10

one month ago ? Maybe – what if it executes 20M times/daily,and can you “normalize dynamic statements ?


Do you proactively monitor application performance or do you rely on user complaints?

– Do you monitor trends to identify potential problems before they occur?

– Do you manually determine, set, and adjust thresholds for monitoringapplications (aka. Baselines)?

– Does your current solution automatically determine what is “normal”?

– Do you spend time reviewing false or short-term “spike” alerts / eventnotifications (as opposed to sustained deviations) ?

– How much time is spent defining /maintaining thresholds?


How do you resolve performance problems?

How do you determine when performance started to degrade?

How long does it take to identify the problematic SQL statements?

Do you know how the statements executed before the performance degraded?

Do you have a log of application performance problems and resolutions?

– Can you compare this log with current problems?

Many customers offload Detector datastore into DB2 tables– Query heaviest plans/packages & manually compare to “baselines”

– No efficient method to re-evaluate baselines when “the world changes”

– How to monitor “standard deviation” and “creeping trend” is complex and cumbersome


Thank you

Jim EndlerSr. Principal Consultant, Technical SalesCA Technologies | 11325 N. Community House Road Suite 550 | Charlotte, NC 28277 Mobile: +1 713 703 5888 | [email protected]


intelligent mainframe management with ca - nedb2ug.com · – expands value of ca sysview –...

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