The document discusses analytical modeling techniques for assessing system performance. It describes choosing a representative baseline period for modeling, such as a peak usage time. Various modeling scenarios are presented, like increasing a workload by 5% or changing hardware, to determine how systems would be affected. Both mainframe and distributed modeling examples are provided that examine impacts of changes to CPU, storage, and workloads on system performance. The document emphasizes gathering accurate system data and validating model results against real systems.

1. ANALYTICAL MODELING AND
CREATING EFFECTIVE BASELINES
Charles Johnson – Senior Solutions Engineer
Bill Bostridge – VP Sales, Data Infrastructure Optimization

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6. Agenda
Modeling Tools and Techniques
What Information do I need?
What questions can a model answer?
Determine the baseline for the modelling question
Modeling scenarios
Recap
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7. “A model is a simplification of
reality, built for a specific purpose”
Models
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8. What Questions can a Model Answer?
Where are my current bottlenecks?
How will increasing workloads affect response time?
How much more work can my systems handle?
What is the degree of tolerance in the forecast?
How sensitive is it to workload growth estimates?
When do I need to upgrade, and what performance benefit will be gained?
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9. Quality of Service for Stakeholders
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10. Analytical Models
Modeling Tools and Techniques
Trending
Consolidation
Vendor
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11. Analytical Models
Analytical modeling
– Multi-class queuing network theory; stable and well understood
– Operational research laws applied to computers
– Known as Queuing Network Models (QNM)
Modest time, effort and 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
Quick sensitivity analyses
Quality of Service to Stakeholder
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12. Analytical Models – Pros and Cons
Pros
– Accurate enough for most planning purposes
– Quick to build and get results
– Does not require detailed expertise
– Completely reproducible results
Cons
– Limited to specific workload mixes at a single point in time
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13. Simulation Models
Discrete Event Simulation (DES) engine
Detailed knowledge of system internals
Even more details to achieve high accuracy
Good instrumentation or expertise to collect data
Expertise to model a system and calibrate the model
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14. Simulation Models
Pros
– Potentially very accurate if set up by an expert
– Can model behavior over time
– Can model detailed system internals such as database buffer pools
Cons
– Potentially very inaccurate if not set up by an expert
– High degree of detailed knowledge and specialist expertise
– Time and effort to set up
– Time to produce a report
– More time to consider all potential scenarios
– Even more time to do sensitivity analysis
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15. Modeling vs. Trending
Trending
– Easy to do
– Scales well for multiple systems e.g. Windows , UNIX
– Easy to alert on
– No relationship to the performance (service) of a system or application
– Looks at just one metric
Modeling
– Relates service to utilization
– View whole system interactions
– ITIL recommends modeling
– Moderate effort
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16. Spreadsheet Analysis
Limitations occur when too much of too many types of data must be analysed
and reported on repeatedly
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http://optimityanz.com/wp-
content/uploads/2014/10/spreadsheet.png

17. Analytical Modeling Workflow
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Modify Model and
Predict Performance
Build and Calibrate
the Model
Current Configuration
Measure Current
Environment
Technical & Business
Inputs
Describe the
expected system
Technical & Business
Inputs Expected
System
Expected Configuration
Measure and
Compare with
Predictions
Actual Inputs
Actual Configuration
Actual Inputs
Actual Configuration

18. What information do I need?
Service Level Agreements (SLA)
Business information, i.e. # of widgets made quota
New applications
New hardware from vendors
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19. Determine the baseline for the modeling question
Time and Duration
– Identify peak times (when future problems may occur)
– Ensure that important workloads are present
Consistent behaviour
– Type of work
– Transaction arrival rate
– Total system loading
Not affected by external events
– Normal availability
– No media failures, looping tasks, etc.
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20. Choosing a modeling period - Peak vs. Average
What method do you want to base the modeling?
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21. Choosing a modeling period
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22. Choosing a modeling period
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23. Choosing a modeling period - Time of Day
Does the time of day affect workloads
Is the environment transactional based
Is over-night batch running into online uptime
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24. Business Transactions vs. CPU Usage
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25. Modeling Scenarios
Change in Workload characteristics
Change in hardware characteristics
Hardware / Merger & Acquisition consolidation
Moving to the Cloud
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MAINFRAME ANALYTICAL MODEL SCENARIO

27. Model Baseline - CPU/CEC Modeling – Hardware & I/O
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28. Increase ProdOnl 5% - CPU/CEC Modeling – Hardware & I/O
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29. Results CPU Change - CPU/CEC Modeling – Hardware & I/O
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30. DISTRIBUTED ANALYTICAL MODEL SCENARIO
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31. Model – oracleq000 10% growth – server change
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32. Model – Linux server change & disk change
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33. Baseline – Hardware & I/O
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34. Model – Increase ProdOnl workload 5%
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35. Results CPU Change based on workload increase
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36. Recap
What is the question you are attempting to answer?
Gather as much data and information as possible
Ask questions and document
Ensure your baseline matches the real world
Compare your modeling results to the actual timeframe
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38. We are Finished
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39. THANK YOU