how to perform capacity management and planning …how to perform capacity management and planning...
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How to perform capacity management and planning on IBM Z
Rebecca Levesque
21st Century Software
CEO/President
Al Hanna
21st Century Software
VP, Technical Sales
Tuesday November 5, 2019
16:45-17:45 Magny Cours
Session OF
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 – 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
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
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.
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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
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
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
Network Feature (SNA)
19
IBM Z Decision Support for CP Features and Components
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
Network Feature (SNA)
20
IBM Z Decision Support for CP Features and Components
Design your own
• Leveraging the same capability as
IBM
Accounting + Custom
Systems Performance
Feature
Distributed Feature
AS/400 Feature
CICS Feature
IMS Feature
Network Feature (SNA)
21
IBM Z Decision Support for CP Features and Components
• 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
Network Feature (SNA)
22
IBM Z Decision Support for CP Features and Components
• 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
Network Feature (SNA)
23
IBM Z Decision Support for CP Features and Components
• iSeries (IBM i)
• Accounting
• Configuration
• Job Statistics
• Messages
• Performance
Accounting + Custom
Systems Performance
Feature
Distributed Feature
AS/400 Feature
CICS Feature
IMS Feature
Network Feature (SNA)
24
IBM Z Decision Support for CP Features and Components
• 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
Network Feature (SNA)
25
IBM Z Decision Support for CP Features and Components
• 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
Network Feature (SNA)
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
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
Automated Data Collection to pseudo GDG
SY
S2
SMF SMF Extractor
SpokeJava Data
Mover
SY
S3
SMF SMF Extractor
SpokeJava Data
Mover Log Stream
Spoke
Hub
36
IZDS/CP v2.1 Architecture
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
Automated Data Transfer
Automated Data Collection to pseudo GDG
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
IZDS/CP v2.1 Architecture
SY
SX
(H
ub
syste
m)
Continuous Collector Data tables
3270 & Cognos Reports
IDAA
3270 & Cognos Reports
Db2
SY
S1
SMF SMF Extractor
SpokeJava Data
Mover
HubJava Data
Receiver
Automated Data Transfer
Automated Data Collection to pseudo GDG
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
38
IZDS/CP v2.1 Architecture
SY
SX
(H
ub
syste
m)
Continuous Collector Data tables
3270 & Cognos Reports
IDAA
3270 & Cognos Reports
Batch Log Collector *
Db2
SY
S1
SMF SMF Extractor
Non-SMF/ Log Data
ExtractNon-SMF/ Log data
SpokeJava Data
Mover
HubJava Data
Receiver
Automated Data Transfer
Automated Data Collection to pseudo GDG
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
* Batch Log Collector can still be used with SMF
Hub
39
IZDS/CP v2.1 Architecture
SY
SX
(H
ub
syste
m)
Continuous Collector Data tables
3270 & Cognos Reports
IDAA
3270 & Cognos Reports
Batch Log Collector *
Db2
SY
S1
SMF SMF Extractor
Non-SMF/ Log Data
ExtractNon-SMF/ Log data
SpokeJava Data
Mover
HubJava Data
Receiver
Automated Data Transfer
Automated Data Collection to pseudo GDG
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
* 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
Automated Data Transfer
IBM Common Data Provider
• 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
Automated Data Transfer
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
Please submit your session feedback!
Thank you!
• Do it online at http://conferences.gse.org.uk/2019/feedback/of
• This session is OF
52
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
LPAR MSU
Consumption by
Container
Monthly View
w/ drill-down
to
Day / LPAR
57
Tailored Fit Pricing Reports
MSU Consumption by Workload and
Service Class
Details on the consumption based on
daily / hourly / timestamp granularity
58
Tailored Fit Pricing Reports
59
MSU Usage Per Hour on a
Month to Month
comparison
MSU Usage Per Day on a
Month to Month
comparison
IBM Z Decision Support for CP Features and Components
• 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
Network Feature (SNA)
61
• 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
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— © 2019 International Business Machines Corporation. No part of
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65
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