how to perform capacity management and planning …how to perform capacity management and planning...

How to perform capacity management and planning on IBM Z

Rebecca Levesque

21st Century Software

CEO/President

[email protected]

Al Hanna

21st Century Software

VP, Technical Sales

[email protected]

Tuesday November 5, 2019

16:45-17:45 Magny Cours

Session OF

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Please note

• IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice and at IBM’s sole discretion.

• Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision.

• The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract.

• The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.

• Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

2

What we will cover today

IBM / 21CSW Partnership

IZDS for Capacity Planning Overview

IZDS for Capacity Planning Forecasting

Key Takeaways

3

IBM Strategic Partnership with 21st Century Software

4

IBM – 21st Century Software: Strategic Partnership

• Based in Wayne, Pennsylvania

• 21st Century Software has been assisting customers since 1992

• IBM and 21st Century Software entered into a strategic partnership

• To create more competitive advantage in the marketplace

• To bring more value to IBM customers by accelerating product roadmap innovation of TDSz

• IBM will continue to sell these products as it does today

• TDSz – IZDS/CP customers will continue to access support through IBM, while all technical

support and development will be performed by 21st Century Software

• 21st Century Software will assist IBM with go-to-market and services

5

Continuous delivery of enhancements…

And we’re just getting started…..

2017

TDSz

v1.8.2

Q2 2018

IZDS v1.9

New processes

added to

simplify

collection of

data and lower

cost of

processing

Q3 2018

Integration

with CDPz

Ability to stream

IZDS curated

data to analytic

platforms

Q4 2018

IZDS for

Capacity

Planning

v2.1

Forecasting

capabilities

added to deliver

predictive

modelling

Q2 2019

Cognos

Analytics

Pricing &

Tailored Fit

High value

dashboard for

KPMs and MSU

consumption

tracking

Q4 2019

CICS

Capacity

Planning

New module

and reports

6

Challenges clients are facing today

7

8

What’s the overarching problem?

Year of Digital Disruption

CIOs are struggling to balance these

two competing pressures, while

managing Risk and Compliance

• Provide stable, secure, high-

performance services

• Deliver, innovative, technology-

intensive services quickly

http://www.gartner.com/smarterwithgartner/six-cio-responsibilities-for-

digital-business-leadership/

IT Operations goals are to improve quality and

reduce cost while supporting growth and change

But…

• 53% of IT Operations cite managing technology

changes as their biggest challenge, especially in

large legacy environments

• 43% feel that insufficient skills and resources are

their biggest issue

• 34% are most concerned about having insufficient

capacity to absorb more change

Source: Gartner Leadership Vision for 2019: Infrastructure and Operations Leader

http://www.gartner.com/smarterwithgartner/six-cio-responsibilities-for-digital-business-leadership/

The mainframe is underlying 72% of customer facing apps, but challenges to manage it

are mounting

• Access to insightful info for managing workloads and problems is not timely

• Predictive analysis needed to make quick decisions based on historical knowledge

• ‘Tribal knowledge’ fading, elongating time to identify and recover from problems

56% of customers have no succession plan for their

mainframe skills

• Organizations face skills issues as they go through a generational shift

• Manual processes place additional burden on staff and increase the risk of error

Companies are spending $1.2-$2.5 billion annually due to

unplanned application outages

• IT is held accountable to maintain availability while managing increasingly complex workloads

• There is no margin of error for outages; whether from batch abends, software upgrades gone wrong or disaster events

9

Digital transformation is impacting all areas of the enterprise

Focus areas for Application Availability include:

• Improved automation

• Faster problem

determination

• Visibility into end to end

performance

• Elimination of unplanned

outages

Business Priorities in a Digital Economy

2018 BMC Mainframe Survey

10

Here’s why you have to fix it……

Source: IDC ‘Five Key Technologies for Enabling a Cyber-Resilience Framework’ – August 2019 and

IDC ‘The Business Value of the Transformative Mainframe’ – August 2019

Digital transformation is challenging traditional views of business resilience.

Digital transformation is the process through which technology is intertwined

throughout the human experience.

If your data isn’t available, it can no longer be productized and monetized - it can no

longer be leveraged for business agility.

Data is critical to business survival.

This makes data integrity and accessibility sacrosanct.

IDC projects that study participants will realize average benefits worth more than

6x what they invest to transform their mainframe platforms.

11

Encryption Everywhere

Industry-first solution to protect sensitive data across your multicloud

Instant Recovery

Industry’s highest level of business uptime to meet SLA

and regulatory compliance

Cloud Native Development

Integrate seamlessly into hybrid multicloud, blockchain and AI

The cloud you want – with the privacy and security you need

IBM z15

Standardized & Flexible for the Cloud Data Center

Modular, scalable and proven cloud-ready infrastructure

12

Analytical Insights are Vital to Enterprise System Health

IBM Z produces a vast amount of high-quality data.

The ability to turn this into timely, actionable information

is vital for…

• Providing a comprehensive view of applications across

the mainframe

• Ensuring performance and continued availability of

critical business applications

• Making accurate strategic decisions to enable the

business to leverage the mainframe for faster time-to-

value and avoid resistance to change

This is where IBM Z Decision Support for CP comes in

13

Typical ApproachCapacity-Financial-Performance Management

• Multiple tools are used

• Data is not consumable immediately

• Non-SMF log data is difficult to capture

• Expertise is required

• Analysis is done after the fact

• Reporting options can be limited

• Homegrown tools are often used

• Forecasting is done with Excel

• Ongoing maintenance requires staff

14

IBM Z Decision Support for Capacity Planning Overview

15

IT Infrastructure Library (ITIL) & IBM Z Decision Support for Capacity Planning

IZDS for CP

PerformanceManagement

FinancialManagement

CapacityManagement

Responsible for measuring

and checking the level of

performance in terms of

productivity and cost

reduction.Ensure that the

capacity of IT

infrastructure is able

to deliver the agreed

service level targets in

a cost effective and

timely manner. Give accurate

and cost-

effective

stewardship of

IT assets and

resources used

in providing IT

Services

16

Leverage structured logs across the enterprise to assess its

current state and feed critical decision-making

AnalyticsIBM i

CICS/Db2/IMS

RACF

WebSphere MQ for z/OS

SMF/RMF TCP/IP for z/OS

WAS

Network

Distributed Systems

How does IBM Z Decision Support for Capacity Planning do it?

17

IBM Z Decision Support for Capacity Planning v2.1

• Ordered via

• 5698-AS1 Announcement

• 5698-ABP Announcement

Included forecasting for …

• CPU MIPS

• LPAR

• Service Class

• MSU *new

• Storage (Memory)

• Channel

• Disk

• Tape

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature

Network Feature (SNA)

Cap

acity Plan

nin

g

18

https://www-01.ibm.com/common/ssi/ShowDoc.wss?docURL=/common/ssi/rep_ca/8/897/ENUS218-528/index.html&request_locale=en

https://www-01.ibm.com/common/ssi/ShowDoc.wss?docURL=/common/ssi/rep_ca/7/897/ENUS219-357/index.html&request_locale=en

IBM Z Decision Support for CP Features and Components

Resource Accounting Feature(Db2 database)

• z/OS System

• z/OS Interval Accounting

• Db2

• DFSMS

• VM Accounting

• WebSphere

• Message Broker

• + more

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


19


Usage and Accounting Collector

• SMF

• DASD

• Tape

• Db2

• CICS

• IMS

• WebSphere

• + more

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


20


Design your own

• Leveraging the same capability as

IBM

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


21


• Key Performance Metrics• z/OS• Db2

• z/OS Performance Management• z/OS System• z/OS Interval Job/Step• RACF• SMS• TCP/IP• z/VM Performance• z Linux• WebSphere …• + more

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


22


• Unix (Sun Solaris, HP-UX, AIX)• Accounting • Configuration• Error • Performance

• Linux (RedHat, SUSE, and TurboLinux)• Performance

• Windows• CPU• Disk• Memory

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


23


• iSeries (IBM i)

• Accounting

• Configuration

• Job Statistics

• Messages

• Performance

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


24


• Key Performance Metrics

• CICS

• CICS Monitoring• includes Omegamon• Analysis by transaction,

application, user

• CICS Statistics

• CICS Transaction and Unit-of-Work

• CICS Transaction Gateway

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


25


• Key Performance Metrics

• IMS

• Full-function transaction analysis

• Fast path transaction analysis

• Mixed Mode transaction analysis

• Program-to-program switching

• Message switching

• Multiple IMS versions

• IMS internal statistics

• IMS Availability

• Shared Message queueAccounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


26

The minimum, most relevant, information needed

to manage performance

• Designed in consultation with IBM SME’s

• System health overview

• Detailed problem determination

• Exception reporting

In some cases …

• Reduces the number of metrics stored in Db2

by up to 90%

• Reduces the CPU & elapsed time for data

collection by more than 90%

Key Performance Metrics

27

The Challenge:

• Reduction in the availability of staff with

Performance and Capacity expertise

• Environments are more complex with

unpredictable workloads

• Businesses require performance and capacity

be managed to meet SLAs and keep costs

down

Using IZDS CP KPM reports to manage by exception

The IZDS CP Approach:

• KPM (Key Performance Metric) reporting

provides the essential information needed

to manage exceptions

• Performance and capacity exceptions are

identified in a timely manner, with less

manual effort28

• New Tablespace Partitioning Automation

• Define one or multiple tablespace profiles

• In a single location

• Can be applied to …

• a specified log type (SMF, IMS)

• a specified component

• an individual table space

• Used by IZDS when components are

installed

Leveraging Db2 for Concurrent Collection

Process data faster and more efficiently!

29

IZDS CP End-to-End ApproachCapacity-Financial-Performance Management

✓ Selective curation of SMF records and

fields

✓ SME recommendations on functions

✓ Business Descriptors - application name

and more

✓ Automatic aggregation - timestamp,

hourly, daily, weekly, monthly

✓ You decide how much history to keep

✓ Extensive base of reports - 1,426 via

ISPF, 600 web, + Cognos / Splunk / ELK

✓ Forecast the right capacity when you

need it

✓ Cost avoidance - don’t upgrade too early

or too late

✓ Optimize application performance and

availability

30

IZDS/CP Cognos Report Samples

Storage Group Allocated Space by Month Workload MIPS Usage by Week31

IZDS/CP Splunk Dashboard Landing Page

32

LPAR CPU Utilization LPAR Avg MIPS by CEC Weekly

Job Maximum CPU Task - Daily Workload Service Class CPU Utilization hourly for the

specified processor type

IZDS/CP Splunk Dashboard Samples

33

CPC CPU Busy, Hourly LPAR CPU Busy, Hourly

Disk Usage by Storage Group, Daily Tape Usage Forecast by Storage Group, Weekly

IZDS/CP ELK Dashboard Samples

34

SY

S1

SMF SMF Extractor

Automated Data Collection to pseudo GDG

SY

S2

SMF SMF Extractor

SY

S3

SMF SMF Extractor

Spoke

35

IZDS/CP v2.1 Architecture

SY

SX

(H

ub

syste

m)

SY

S1

SMF SMF Extractor

SpokeJava Data

Mover

HubJava Data

Receiver

Automated Data Transfer


SY

S2

SMF SMF Extractor

SpokeJava Data

Mover

SY

S3

SMF SMF Extractor

SpokeJava Data

Mover Log Stream

Spoke

Hub

36


SY

SX

(H

ub

syste

m)

Continuous Collector Data tables

3270 & Cognos Reports

Db2

SY

S1

SMF SMF Extractor

SpokeJava Data

Mover

HubJava Data

Receiver



SY

S2

SMF SMF Extractor

SpokeJava Data

Mover

SY

S3

SMF SMF Extractor

SpokeJava Data

Mover

Log Collector adapted for continuous operation

Log Stream

Spoke

Hub

37


SY

SX

(H

ub

syste

m)



IDAA


Db2

SY

S1

SMF SMF Extractor

SpokeJava Data

Mover

HubJava Data

Receiver



SY

S2

SMF SMF Extractor

SpokeJava Data

Mover

SY

S3

SMF SMF Extractor

SpokeJava Data

Mover


Log Stream

Spoke

Hub

38


SY

SX

(H

ub

syste

m)



IDAA


Batch Log Collector *

Db2

SY

S1

SMF SMF Extractor

Non-SMF/ Log Data

ExtractNon-SMF/ Log data

SpokeJava Data

Mover

HubJava Data

Receiver



SY

S2

SMF SMF Extractor

SpokeJava Data

Mover

SY

S3

SMF SMF Extractor

SpokeJava Data

Mover


Log Stream

Spoke

* Batch Log Collector can still be used with SMF

Hub

39


SY

SX

(H

ub

syste

m)



IDAA


Batch Log Collector *

Db2

SY

S1

SMF SMF Extractor

Non-SMF/ Log Data

ExtractNon-SMF/ Log data

SpokeJava Data

Mover

HubJava Data

Receiver



SY

S2

SMF SMF Extractor

SpokeJava Data

Mover

SY

S3

SMF SMF Extractor

SpokeJava Data

Mover


Log Stream

Spoke

* Batch Log Collector can still be used with SMF

Hub

40

IZDS/CP v2.1 ArchitectureSplunk Dash

Boards

ELK DashBoards

Log Stream

PublicationJava Data

Mover


IBM Common Data Provider

IBM Z Decision Support for Capacity Planning Forecasting

41

• Data collection and preparation

• Manual process to prepare formal reporting and trends

• Difficulty predicting future hardware growth plans

• Number of ad hoc requests

• Loss of skills

• Lack of time

Capacity Management Challenges

42

Thresholds

• User-defined thresholds

Exceptions

• Historical

• Determine when historic usage was unusual

• Determine when historic usage exceeded a

threshold

• Real-time

• Identify when real time usage is unusual

• Identify when real time usage exceeds an

existing forecast, or threshold

• Forecasting

• Determine if/when forecast value will cross

a threshold

IZDS/CP Threshold and Exceptions

Exception ID Threshold Description

LPAR_BUSY 90 LPAR Busy > 90%

CHAN_BUSY 50 Channel Busy > 50%

WLM_PI_MAX 1.1 Performance Index > 1.1

WLM_PI_MIN 0.7 Performance Index <= 0.7

STOR_AVLBL 768000Storage Frames Available <

768000

CF_BUSY 50Coupling Facility Busy >

50%

43

IZDS Capacity Planning Architecture

Hub

Continuous Collector

Data tables

Batch Log Collector

Db2

Splunk DashBoards

ELK DashBoards

Log Stream

PublicationJava Data

Mover

NewForecast Tables

Forecast results stored in Db2

tables

New Reports

Reports combine history with forecast

New Forecaster

(Java)

Forecasting Algorithms

+LSPR

+Dynamic Capacity

Forecaster queries historical

data


IBM Common Data Provider

44

Forecaster - Processing Automation

• Query historic usage IZDS data tables via JDBC connection

• Prepare historical data for forecasting

• Run forecasting algorithms (Apache Commons Maths library)

• Write forecasting results to Forecasting data tables via JDBC

• Write forecasting results to publication log stream for CDP integration

For all provided, or customer-written components (meaning any structured data

that IZDS can collect, including Distributed), it is a relatively easy process to

build a new forecast (writing the result to a new DB2 table).

45

IZDS/CP Forecast Report Samples

CEC MIPS Weekly Forecast

CP Application MIPS Forecast by Week CP CEC MIPS Capacity and Usage Monthly

Actual vs Forecast report46

Current Capability

• CPU, Memory and Storage (Disk and Tape) utilization Forecast reports

• Forecasting based on statistical algorithms (multiple choice)

• Current resource utilization versusforecast reports

• Exceptions vs historical patterns and forecast tracking

• User-defined Threshold

Future Considerations

• zIIP Enabled Collector

• Augment Historical Data

• Automatic Algorithm Selection

• Simulation of HW Configuration

Changes

• Simulation of Workload Changes

IZDS/CP Future Considerations* Disclaimers Duly Noted

47

Support for Tailored Fit Pricing

• Tailored Fit Pricing – announced May 14 – is a revolutionary new pricing model that eliminates the need for capping and provides a complete alternative to the R4HA.

• New reports in IZDS for CP supports customers adopting Tailored Fit Pricing:

• Track Container MSU usage by month to know when your annual entitlement is met

• Investigate high monthly MSU usage with automated drill-down reporting

• Provide Comparison Reports to track actual usage vs expected usage based on historic trends

• Forecast future MSU usage to project variable charges

Now available!

48

Learn More

Tailored Fit Pricing: How to manage workload in a world without capping

Thursday 7 Nov 2019 | 11:45-12:45 Magny Cours | Session #OO

Speaker: Nathan Brice

49

Key

Takeaways

▪ IBM Z Decision support for Capacity Planning is an End to

End tool for Capacity Planning and Performance

Management

▪ Information is continuously collected and curated near

real-time for immediate availability of reports , huge volumes

of data are distilled into relevant, meaningful information

▪ Predictive capabilities to ensure business application

availability as well as business-oriented reporting are

available in IBM Z Decision Support for Capacity Planning

49

Additional Resources

IBM Z Decision Support Information

▪www.ibm.biz/IZDSInfo

IBM Z Decision Support for Capacity Planning Information

▪www.ibm.biz/IZDSCapPlanInfo

IBM Z Software Newsletter – Operations & Management Edition

▪www.ibm.biz/ZOperations

IBM Z Decision Support: Streaming curated Z operations data to Splunk

▪ https://youtu.be/IwwxmU1kmfw

50

http://www.ibm.biz/IZDSInfo

http://www.ibm.biz/IZDSCapPlanInfo

http://www.ibm.biz/ZOperations

https://youtu.be/IwwxmU1kmfw

Questions?

51

51

Please submit your session feedback!

Thank you!

• Do it online at http://conferences.gse.org.uk/2019/feedback/of

• This session is OF

52

Backup Material

53

What challenges does software pricing bring for Performance Analysis and Capacity Planning?

We need to understand the

level of our MSU

consumption on a daily

basis so we can

understand impacts of

workload changes and the

costs associated with it

Now we’re looking at all workloads at all

times to optimize our performance and

drive down costs. Understanding all our

zIIP-eligible workloads will help us

As a capacity planner, I need

to assist in projecting the

needs for growing workloads

on the mainframe and fit in

with our MSU allocation

We used to cap workloads

to limit our costs. Is there

any benefit to this now

we’ve switched to an MSU

consumption model?

54

Rolling 4-Hour Average Reports

IZDS CP still provides insight into the

R4HA as it always has…

…including drill down to a product’s

contribution to the R4HA

55

Tailored Fit Pricing Reports

Overall consumption so far, versus

annual projections

56


LPAR MSU

Consumption by

Container

Monthly View

w/ drill-down

to

Day / LPAR

57


MSU Consumption by Workload and

Service Class

Details on the consumption based on

daily / hourly / timestamp granularity

58


59

MSU Usage Per Hour on a

Month to Month

comparison

MSU Usage Per Day on a

Month to Month

comparison

Tailored Fit Pricing Reports: Container MSU Monthly Forecast

60


• Frame Relay Utilization• LAN Utilization• Line Utilization• NCP Transit Time (ITMNP)• NCP Utilization• NEO Utilization• NetView FTP • NTRI Utilization• ODLC Utilization• PU Utilization• RTM Response Time• SNMP routers• VTAM Statistics• X.25 Utilization …• + more

Accounting + Custom

Systems Performance

Feature

Distributed Feature

AS/400 Feature

CICS Feature

IMS Feature


61

IZDS for Capacity Planning: zIIP-Eligible Workloads

62

• Spoke and Hub

• The IBM Z Decision Support Db2 database lives on the Hub

• Data is gathered on the Spoke systems and sent to the Hub

• Log streams and TCP/IP

• Log streams on the Spoke and Hub systems to buffer data

• TCP/IP (with SSL) for inter-system communication

• Continuous Collection

• Runs as a Started Task

• Aggregates data in memory and commits it to Db2 after its aggregation interval

Continuous Collection Terminology

63

• SMF Extractor

• A list of the SMF record types it needs to trap and the log stream to write them to

• Data Mover as Sender

• The log stream to read the SMF data from and the TCP/IP details for the Hub receiver

• Data Mover as Receiver

• TCP/IP port to listen on and the log stream to write the SMF data to

• Continuous Collector

• The log stream to read from and some new policy to control its collection

Continuous Collection Terminology

64

Notices and disclaimers

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— Information in these presentations (including information relating

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In some cases, a product may not be new and may have been

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