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The presentation was given during the Nagios World Conference North America held Sept 25-28th, 2012 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/nwcna

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- 1. Data Analysis Nicholas Scott nscott@nagios.com
- 2. Disclaimer Math may occur later. I apologize in advance. 2012 2
- 3. Abstract Introduction Capacity Planning Component Features Different Forecasting Methods When to use RRD Analysis Tool Statistics Pillow Talk 2012 3
- 4. Introduction Nagios Data Gathering Attributes SO MUCH DATA (TOO MUCH?) Generally noisy Sources usually not simple How many factors are affecting service X on a given host Y? We have data showing X is like this but why? 2012 4
- 5. Capacity Planning Terminology Residuals – Variation that exists after fitting Period – A frame of time where a pattern cycles through a complete iteration Example: 2012 5
- 6. Capacity Planning/home/nscott/Documents/NWC Presentations/DataAnalytics/capacityplanning/capacityplanning.mp4 2012 6
- 7. Capacity Planning Holt-Winters Great next-step forecasting for complex systems 2012 7
- 8. Capacity Planning Gets Dicey for anything more, tradeoffs 2012 8
- 9. Capacity Planning Least Squares Better for simple trending, obviously Finds trend line that minimizes the sum of the residuals squared Less computationally expensive than HW 2012 9
- 10. Capacity Planning Good choice for noisy data Possible future mean value 2012 10
- 11. Capacity Planning Linear Algebra is fun Linear Algebra is grindy Linear Algebra is a great way to really think about algorithms RRD Python abstraction class is available 2012 11
- 12. Capacity Planning Quadratic/Cubic Fit Naive Experimental Fits a polynomial of given order to data 2012 12
- 13. Capacity Planning For quadratic or cubic datasets User decision 2012 13
- 14. RRD Analysis Tool Goals General stats, mean, variance, etc Also do derivatives, multiple order derivatives Bivariate correlation Dependencies: Python >= 2.4 numpy, rrdtool, scipy, matplotlib, mako 2012 14
- 15. RRD Analysis Tool Example running of this thing: ./analyze.py -H localhost -S Current_Load -s 2012 15
- 16. RRD Analysis Tool Why do you want to smooth your stuff? Noise noise noise Comedy Option: Pretty graphs Mean Stddev Variance 2012 16
- 17. RRD Analysis Tool Derivatives Δx Quick refresher: Δy Actual form well use: y t − y t−1 y t − yt −1 = t t −t t−1 RRD Resolution 2012 17
- 18. RRD Analysis Tool Uses? Relateable to physics? Position Velocity Acceleration Jerk (seriously) 2012 18
- 19. RRD Analysis Tool Example, first derivative on CPU Load: analyze.py -H localhost -S Current_Load -d 1 2012 19
- 20. RRD Analysis Tool Direct use case? Back to bytes/sec 2012 20
- 21. RRD Analysis Tool Second derivative (acceleration) analyze.py -H localhost -S Root_Partition -d 1,2 2012 21
- 22. RRD Analysis Tool Bivariate Analysis Compare two possibly related variables Define a relationship Graph them on the same graph Find Pearsons Correlation Coefficient 2012 22
- 23. RRD Analysis Tool Example: analyze.py -H localhost,localhost -S _HOST_,PING 2012 23
- 24. RRD Analysis Tool Example: analyze.py -H localhost,localhost -S HTTP,Current_Load 2012 24
- 25. RRD Analysis Tool Example: analyze.py -H localhost,localhost -S Current_Load,Root_Partition 2012 25

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