Today’s organizations continue to search for ways that technology can improve their IT business processes and provide competitive advantage. Machine learning is one of the tools that has begun to deliver breakthroughs in delivering the sort of improvements that companies are continually searching to find. There is an increased use of machine learning in capacity management and workload optimization processes. We see an increasing desire in the marketplace for a better understanding of machine learning and how it works. Specifically, there is a need to understand how combining machine learning and capacity management work together to dramatically improve capacity management efforts. View this webcast to learn: - What is machine learning? - Key terminology used when working with machine learning - Benefits of machine learning for capacity management

1. Is Machine Learning the Solution to Your
Capacity Management Challenges?
Charles Johnson
Senior Technical Consultant
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2. Agenda
• Capacity Management Overview
• Machine Learning Overview
• Machine Learning Algorithms
• Use Case
• Summary

3. “
”
“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

4. Capacity Management Overview
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5. • 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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6. • Trends
• What-If Trend
• Analytical Modelling
• Machine Learning
• Simulation Modelling
• Benchmarking
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Capacity Management Techniques

7. Machine Learning
Overview

8. • 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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9. 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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Machine Learning Approaches

10. 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

11. 11
Machine Learning Approaches

12. Self-Driving Vehicles Automated Recommendations Computer Translation
Machine Learning Uses
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13. The Machine Learning Cycle
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14. Machine Learning Algorithms

15. • Linear Regression
• Logistic Regression
• Decision Tree
• kNN (k-Nearest Neighbors)
Common Machine Learning Algorithms
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16. • 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

17. 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

18. Capacity
Management

19. 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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20. Join Capacity Management -
Machine Learning

21. • 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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22. Use Case

23. • 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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24. Analyze Environment by Region
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25. 25
Project Resource Consumption

26. Summary

27. • 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

28. References
Machine Learning for dummies – IBM
(Judith Hurwitz & Daniel Kirsch)
M. Asokan
Syncsort
Chief Architect, Distributed Systems & Big Data
masokan@Syncsort.com
John Greenwood
Syncsort
Technical Architect
John.Greenwood@Syncsort.com
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29. Questions