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You’ve instrumented your system and application to the hilt. You can now “measure all the things”. Your team has set up thousands of metrics collecting millions of data points a day. Now what?

Analyzing this mountain of data and extracting signal from the noise is not easy, so most IT ops teams only keep an eye on a small fraction of the metrics they collect, or they run some simplistic analytics that don’t generate any useful information. The choice of what analytic method to use ranges from simple statistical analysis to sophisticated machine learning techniques. And one algorithm doesn’t fit all data.

While the more advanced algorithms require math nerds with PhD’s to develop, there are some basic statistical methods that anyone can implement and they can provide surprisingly valuable insights. In this talk, Toufic will show you how to determine the distribution of your data and explain why this is important. We will then explore a few statistical techniques that are appropriate for the most common distributions and discuss their pros and cons. Yes, there will be math in this talk but you will be able to follow along. The goal is for you to walk away with at least one technique that you can apply right away in your monitoring environment to improve the signal to noise ratio and get information out of your data.

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- 1. 1 Some Simple Math to Get Signal out of your data noise #VelocityConf 25.06.2014 Toufic Boubez, Ph.D. Co-Founder, CTO Metafor Software toufic@metaforsoftware.com
- 2. 2 Preamble • I lied: There is no “simple” math for Anomaly Detection! • I usually beat up on parametric, Gaussian, supervised techniques – This talk is to show some alternatives – Only enough time to cover a couple (four, really) of relatively simple but very useful techniques – Oh, and I will actually still start up by beating up on the usual suspects, but don’t despair, there’s good stuff towards the end • Note: all real data • Note: no y-axis labels on charts – on purpose!! • Note to self: remember to SLOW DOWN! • Note to self: mention the cats!! Everybody loves cats!!
- 3. 3 • Co-Founder/CTO Metafor Software • Co-Founder/CTO Layer 7 Technologies – Acquired by Computer Associates in 2013 – I escaped • Co-Founder/CTO Saffron Technology • IBM Chief Architect for SOA/Web Services • Co-Author, Co-Editor: WS-Trust, WS- SecureConversation, WS-Federation, WS-Policy • Building large scale software systems for >20 years (I’m older than I look, I know!) Toufic intro – who I am
- 4. 4 Wall of Charts™
- 5. 5 The WoC side-effects: alert fatigue “Alert fatigue is the single biggest problem we have right now … We need to be more intelligent about our alerts or we’ll all go insane.” - John Vincent (@lusis) (#monitoringsucks) We have forensic tools for analytics after the fact BUT we need to KNOW that something has happened! We need alerts!
- 6. 6 Watching screens cannot scale
- 7. 7 Time to turn things over to the machines!
- 8. 8 Attempt #1: static thresholds … • Roots in manufacturing process QC
- 9. 9 … are based on Gaussian distributions • Make assumptions about probability distributions and process behaviour – Data is normally distributed with a useful and usable mean and standard deviation – Data is probabilistically “stationary”
- 10. 10 Three-Sigma Rule • Three-sigma rule – ~68% of the values lie within 1 std deviation of the mean – ~95% of the values lie within 2 std deviations – 99.73% of the values lie within 3 std deviations: anything else is an outlier
- 11. 11 Aaahhhh • The mysterious red lines explained mean 3s 3s
- 12. 12 Stationary Gaussian distributions are powerful • Because far far in the future, in a galaxy far far away: – I can make the same predictions because the statistical properties of the data haven’t changed – I can easily compare different metrics since they have similar statistical properties • Let’s do this!! • BUT… • Cue in DRAMATIC MUSIC
- 13. 13 Then THIS happens
- 14. 14 3-sigma rule alerts
- 15. 15 Or worse, THIS happens!
- 16. 16 3-sigma rule alerts
- 17. 17 WTF!? So what gives!? • Remember this?
- 18. 18 Histogram – probability distribution
- 19. 19 Histogram – probability distribution
- 20. 20 Attempts #2, #3, etc: mo’ better thresholds • Static thresholds ineffective on dynamic data – Thresholds use the (static) mean as predictor and alert if data falls more than 3 sigma away • Need “moving” or “adaptive” thresholds: – Value of mean changes with time to accommodate new data values, trends, periodicity
- 21. 21 Moving Averages “big idea” • At any point in time in a well-behaved time series, your next value should not significantly deviate from the general trend of your data • Mean as a predictor is too static, relies on too much past data (ALL of the data!) • Instead of overall mean use a finite window of past values, predict most likely next value • Alert if actual value “significantly” (3 sigmas?) deviates from predicted value
- 22. 22 Moving Averages typical method • Generate a “smoothed” version of the time series – Average over a sliding (moving) window • Compute the squared error between raw series and its smoothed version • Compute a new effective standard deviation (sigma’) by smoothing the squared error • Generate a moving threshold: – Outliers are 3-sigma’ outside the new, smoothed data! • Ta-da!
- 23. 23 Simple and Weighted Moving Averages • Simple Moving Average – Average of last N values in your time series • S[t] <- sum(X[t-(N-1):t])/N – Each value in the window contributes equally to prediction – …INCLUDING spikes and outliers • Weigthed Moving Average – Similar to SMA but assigns linearly (arithmetically) decreasing weights to every value in the window – Older values contribute less to the prediction
- 24. 24 Exponential Smoothing techniques • Exponential Smoothing – Similar to weighted average, but with weights decay exponentially over the whole set of historic samples • S[t]=αX[t-1] + (1-α)S[t-1] – Does not deal with trends in data • DES – In addition to data smoothing factor (α), introduces a trend smoothing factor (β) – Better at dealing with trending – Does not deal with seasonality in data • TES, Holt-Winters – Introduces additional seasonality factor – … and so on
- 25. 25 Example 1
- 26. 26 Holt-Winters predictions
- 27. 27 Example 2
- 28. 28 Exponential smoothing predictions
- 29. 29 Hmmmm, so are we doomed? • No! • ALL smoothing predictive methods work best with normally distributed data! • But there are lots of other non-Gaussian based techniques – We can only scratch the surface in this talk
- 30. 30 Trick #1: Histogram!
- 31. 31 THIS is normal
- 32. 32 This isn’t
- 33. 33 Neither is this
- 34. 34 Trick #2: Kolmogorov-Smirnov test • Non-parametric test – Compare two probability distributions – Makes no assumptions (e.g. Gaussian) about the distributions of the samples – Measures maximum distance between cumulative distributions – Can be used to compare periodic/seasonal metric periods (e.g. day-to-day or week-to-week) http://en.wikipedia.org/wiki/Kolmogorov%E2% 80%93Smirnov_test
- 35. 35 KS with windowing
- 36. 36
- 37. 37
- 38. 38
- 39. 39
- 40. 40
- 41. 41 KS Test on difficult data
- 42. 42 Trick #3: Box Plots / Tukey • Again, need non-parametric method: – Does not rely on mean and standard deviation • When you can’t count on good old Gaussian: – Median is always a great alternative to the mean – Quartiles are an alternative to standard deviation • Q1 = 25% Quartile (25% of the data) • Q2 = 50% Quartile == Median (50% of the data) • Q3 = 75% Quartile (75% of the data) • Interquartile Range (IQR) = Q3 – Q1
- 43. 43 Example: box plots and fences for a Gaussian http://en.wikipedia.org/wiki/Interquartile_range
- 44. 44 IQR method for streaming time series • IQR method works well for some non-normal distributions – Generates continuously adaptive fences at (Q1 - 1.5xIRQ) and (Q3 + 1.5xIQR) – Adjusted box plot uses fences at (Q1 - 1.5xIRQ) and (Q3 + 1.5x IQR) • Method: – As time series is streaming, for every window: • Re-compute quartiles • Re-compute IQR, fences • Determine if any outliers • Repeat
- 45. 45 Example 1
- 46. 46 Example 1 – Good
- 47. 47 Example 2
- 48. 48 Example 2 – Bad
- 49. 49 Trick #4: Diffing/Derivatives • Often, even when the data itself is not stationary, its derivatives tends to be! • Most frequently, first difference is sufficient: dS(t) <- S(t+1) – S(t) • Can then perform some analytics on first difference
- 50. 50 CPU time series
- 51. 51 Its first difference – possible random walk?
- 52. 52 Trick #5: Neural Networks • Really? • No time – To Be Continued!
- 53. 53 We’re not doomed, but: Know your data!! • You need to understand the statistical properties of your data, and where it comes from, in order to determine what kind of analytics to use. – Your data is very important! – You spend time collecting it so spend time analyzing it! • A large amount of data center data is non- Gaussian – Guassian statistics won’t work – Use appropriate techniques
- 54. 54 More? • Only scratched the surface • I want to talk more about algorithms, analytics, current issues, etc, in more depth, but time’s up!! – Come talk to me or email me if interested. • Office Hour: Tomorrow at 11:30 Booth #801 • Thank you! toufic@metaforsoftware.com @tboubez
- 55. 55 Oh yeah, and we’re hiring! In Vancouver, Canada

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