Pre-Con Ed: Operational Intelligence – Monitoring the Past to Better Understand Today

1. World®
’16
Mainframe Operational Intelligence
– Monitoring the Past to Better
Understand Today
David Myers – Senior Principal Product Manager –
Intelligent Mainframe – CA Technologies
MFX145E
MAINFRAME

2. 2 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
For Informational Purposes Only
Terms of this Presentation
© 2016 CA. All rights reserved. All trademarks referenced herein belong to their respective companies.
The content provided in this CA World 2016 presentation is intended for informational purposes only and does not form any type of
warranty. The information provided by a CA partner and/or CA customer has not been reviewed for accuracy by CA.

3. 3 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Abstract
Join us to learn how the new release of CA SYSVIEW makes it
easier to access and analyze historical performance data to
better understand current issues and source of problems.
David Myers
CA Technologies
Sr. Principal Product
Manager

4. 4 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Agenda
HISTORICAL ANALYSIS
GREEN-HIGHWAYS: LEARNING ALGORITHMS AND ANALYSIS
1
2

5. 5 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Mainframe Team Center – Operational Intelligence
Detect abnormal patterns of operation, so you can take action before the fire starts
Key Features
§ Data Streaming APIs
§ Historical Data Warehouse
§ Historical Analysis tools
§ Dynamic Baselining and
Threshold Management
§ Anomaly Detection
§ Alarm Management and
Notifications
§ Appliance-Based Install

6. 6 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Historical Analysis
Analysis window
expansion/compression
Time Travel
Multi-metric visualization
Tools Pane
Historical Overview

7. 7 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
DEMO

8. 8 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
So What’s “Normal”?

9. 9 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Goals for Predictive Analytics
1. Dynamically and automatically determine what is typical
operational state for a given system
2. Generate alerts for abnormal scenarios, while reducing
false-alarms for known variance in behavior
– Business cycles (e.g., End-of-Quarter processing, Black Friday,
Statement generation, etc.)
– Natural volatility

10. 10 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
From Historical Data To Quantitative Prediction
If we wish to know if a metric is acting typically, we can compare
present values to previous values
– Provides historical context

11. 11 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
From Historical Data To Quantitative Prediction
Taking this further, we can use historical values to make a
prediction about what range a metric will take a what time.
– Not just if it is unusual, but quantitatively how unusual it is.

12. 12 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Predictive Analytics with Operational Intelligence
Components:
§ Wavelet decomposition
§ Kernel Density Estimator
(KDE)
§ Exponentially-Weighted
Moving-Average (EWMA)
Historical
Input Data,
Same Stage of
Business Cycle
Wavelet
Decomposition
Exponentially-
Weighted
Moving-
Average
Kernel Density
Estimator
Predictive Model
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

13. 13 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
From Predictions To Alerts
§ Utilize Historical data
§ Define bands of Likely and
unlikely values
§ Map real time metric streams
against system defined normal
§ Multi-point alerts generated
using industry-standard
Western-Electric rules
§ Make static thresholds optional!
Unlikely
Most Likely
Metric
Time
Typical
Volatility
Anomaly
Tasks ready to be dispatched
Less Likely

14. 14 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Prediction Intervals vs Mean and Standard Deviation
KDE Mean and Standard deviation
For any distribution, including those which
have multiple peaks, or are asymmetric
Presumes symmetric bell-curve distribution
Size of the +/- bands may be equal or
unequal based on data
All +/- bands are of equal width
Higher computation time/ storage Lower computation time/ storage
Robust against rare, extreme values Distorted by rare, extreme values

15. 15 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Differential Analysis
Prediction Bands
Anomaly States
Detail drill-down
Metric tracker
Green Highway Visualization Toggle

16. 16 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
DEMO

17. 17 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Questions?

18. 18 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Must See Demos
Intelligent
Mainframe:
Advanced
Analytics
CA Design Vision
for Intelligent
Mainframe
Optimizing
Mainframe
Operations
Intelligent
Mainframe:
End-To-End
Performance
Intelligent
Mainframe:
Automation

19. 19 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Smart Bar
Intelligent Mainframe and
Advanced Analytics
Talk to Analytics Experts at Mainframe Smart Bar

20. 20 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Recommended Sessions
SESSION # TITLE DATE/TIME
MFT158S
Intelligent Mainframe Management & the Drive Towards
No-Touch Automation
11/16/2016 at 12:45 pm
MFT167T
Mainframe Team Center – Event Management and Automation
Sneak Peek
11/16/2016 at 3:45 pm
MFX161S
Intelligent Mainframe Management:
The Evolution of Expert Systems
11/16/2016 at 4:30 pm
MFT169T Get the Entire Picture: End-to-End Infrastructure & App Monitoring 11/17/2016 at 11:30 am
MFX162S Crack Open the Big Black Box for Faster Problem Remediation 11/17/2016 at 12:45 pm
MFX163S Modern System Intelligence: Making the Data do the Work 11/17/2016 at 1:45 pm
MFT159P
Panel: Working Smarter Not Harder with Embedded Mainframe
Intelligence
11/17/2016 at 4:30 pm

21. 21 © 2016 CA. ALL RIGHTS RESERVED.@CAWORLD #CAWORLD
Stay connected at communities.ca.com
Thank you.

22. @CAWORLD #CAWORLD © 2016 CA. All RIGHTS RESERVED.22 @CAWORLD #CAWORLD
Mainframe
For more information on Mainframe, please visit:
http://cainc.to/9GQ2JI