Today, capacity management within the enterprise continues to evolve. In the past, we were focused on the hardware – but now we are focused on the services. With that in mind, the amount of data available has increased significantly and has become difficult for individuals to sort through. It is apparent that to be successful in this discipline, we need the machines to do more of the heavy lifting. This includes automatically creating reports, calling out anomalies and producing forecasts. The intuition of the human computer is imperative to the success. View this webinar on-demand where we discuss: • The strengths and weaknesses of capacity management with and without machine learning • What machine learning can provide throughout the process • The benefits of using capacity management and machine learning within your organization

1. The Fine Art of Combining Capacity
Management with Machine Learning
Charles Johnson
Senior Solution Consultant
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2. Housekeeping
Webcast Audio
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Questions Welcome
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using the chat window.
• We will answer them during our Q&A session following the
presentation.
Recording and slides
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email following the webcast with a link to download
both the recording and the slides.
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3. Agenda
1 Capacity Management Overview
2 Machine Learning Overview
3 Algorithms and Analytics
4 Use Case
5 Wrap Up

4. “
”
“As one Google Translate engineer put it, "when you go from
10,000 training examples to 10 billion training examples, it all
starts to work. Data trumps everything.”
Garry Kasparov, Deep Thinking: Where Machine Intelligence Ends and Human
Creativity Begins

5. Capacity Management Overview
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6. • Ensure the right level of ITI investment
• Identify and resolve bottlenecks
• Evaluate tuning strategies
• Improve and report/publish performance
• “Right-size” or “consolidate”
• Ensure accurate and timely procurements
• Ensure effective service level management
• Plan for workload growth, new apps / sites
• Avoid performance disasters
Capacity Management objectives
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7. Capacity Management Inputs and Outputs
Inputs Outputs
Sub-Process
Business Capacity
Management
Service Capacity
Management
Resource Capacity
Management
Technology
SLAs
Business Plans
Operations
Budgets…
Capacity Plan
SLA guidelines
Thresholds
Charging
Audits…
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8. Ensuring adequate capacity
• Now (no capacity-related Incidents)
• In the future
Performance Monitoring
• Services
• Hardware Resources
Tuning
• To provide best QOS “now”
Forecasting resource demands and service levels
• New Applications
• Modelling
Producing the Capacity Plan
• To provide best QOS “in the future”
Capacity Management Tasks
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9. • Trends
• What-If Trend
• Analytical Modelling
• Machine Learning
• Simulation Modelling
• Benchmarking
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Capacity Management Techniques

10. Purpose of Capacity Management
Understand your workloads and implement
continuous system optimization equals
“Stable IT Service” and “Cost Saving”
Increase
Bad
Good
Service
（Response time）
Workload
Few
ManyDecrease
Resource
（Cost）
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11. Machine Learning Overview

12. • The ability for a system to take basic knowledge
and apply that knowledge to new data
• The ability to find unknowns in data
• Main points
• Learning
• Pattern detection
• Follow the data
• Self-programming
What is Machine Learning?
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13. Machine
Learning
Approaches
Supervised Learning
• Established set of data
• Data is classified
• Find patterns in the data
Unsupervised Learning
• Massive amounts of data
• Data is not classified or labeled
• Find patterns in the data
Other approaches
• Reinforcement learning
• Neural networks / Deep Learning
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14. Descriptive Analytics
• Current reality
• Historical context
• Aggregates data for insights
Predictive Analytics
• Anticipate changes by understanding patterns
• Constantly needs new data
• Looks into the future
Prescriptive Analytics
• New for machine Learning
• Combination of business rules, machine learning and computational modelling
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Forms of Data Analysis

15. Machine
Learning
Approaches
Supervised
Based on its color/shape/weight…
Unsupervised
How the different fruits can be classified
inside your grocery store?
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Is that “fruit” an apple?
There is a bunch of
different fruits
Supervised
vs
Unsupervised

16. Machine Learning Uses
Self-Driving Vehicles Automated
Recommendations
Computer Translation
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17. The Machine Learning Cycle
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18. Algorithms and Analytics

19. Machine Learning Algorithms

20. • Linear Regression
• Logistic Regression
• Decision Tree
• kNN (k-Nearest Neighbors)
Common Machine Learning Algorithms
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21. • Predict scores on one variable from the scores
on a second variable
• Study the relationship between real values based
upon continuous variables
• Create the best fitting straight line based on the data
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Linear Regression

22. Should I play Golf?
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Decision Tree
Outlook
Overcast
Rain
Sunny
Low
Humidity Wind
High True False
Yes
Yes
Yes
No No

23. Capacity Management

24. Trending
• OK for utilizations, business volumes
• Useless for service levels (response time)
Analytical models
• Quick and easy to set up
• Potentially very accurate
Simulation models
• Time-consuming and difficult to set up
• Potentially more accurate
Benchmarks and Workload Generators
• Perfect, but expensive and complicated (or impossible)
• Required depending on industry or question to answer
Forecasting Techniques
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25. • Multi-class queuing network theory (QNM)
• Modest time, effort & expertise to model a system
• Attainable accuracy OK for business decisions
• Few metrics, given automatic data collection
• Few parameters for What-If changes
• Quick scenario evaluations and sensitivity analyses
Analytical Models
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26. Join Capacity Management -
Machine Learning

27. • Need data to make it work
• More data the best (Big Data)
• Need to understand and trust the data
• Remove assumptions and bias
• Reduce time to analyze data
Join Capacity Management - Machine Learning
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28. Capacity Management
Focused Data Sources
Data Sources • SMF Record 70 – 79 RMF
• SMF Record 14, 15, 17 – Dataset Activity
• SMF Record 30 - Job Detail
• SMF Record 100, 101, 102 – DB2
• SMF Record 110 – CICS
• SMF Record 115, 116 – WebSphere for MQ
• Windows Perfmon
• VMWare vCenter
• UNIX /Linux mpstat, sar, iostat, vmstat
• Cloud performance statistics
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29. Use Case

30. • What is the problem you are attempting to solve?
• What data is available?
• Do you have a representative period of time?
• What is the “Story” you are attempting to tell?
Capacity Management Use Case
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31. Analytical Model

32. Baseline LPAR Modeling – Hardware & I/O
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33. Increase ProdOnl 5% LPAR Modeling – Hardware & I/O
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34. Results CPU Change LPAR Modeling – Hardware & I/O
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35. Machine Learning

36. Analyze Environment by Region
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37. 37
Project Resource Consumption

38. 38
Show Resource Consumption vs. Trend

39. Wrap Up

40. • M
a
c
• Machine Learning provides value to Capacity Management
• Reduce time spent analyzing data
• Follow the data
• Understand the data
• Trust the data
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Take Away

41. 41
Enhancing CM Process
Humans + Computers > Humans Alone Computers Alone

42. • Machine Learning for dummies – IBM (Judith Hurwitz & Daniel Kirsch)
• M. Asokan - Chief Architect, Distributed Systems & Big Data, Syncsort, Inc.
• John Greenwood - Technical Architect, Syncsort, Inc.
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References