Velocity Europe 2013: Beyond Pretty Charts: Analytics for the cloud infrastructure.

transcript

Beyond Pretty ChartsAnalytics for the Cloud Infrastructure

Velocity Europe 2013

Toufic Boubez, Ph.D.Co-Founder, CTOMetafor Softwaretoufic@metaforsoftware.com@tboubez

Toufic intro – who I am

• 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• 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!)

Genesis of this talk

• Evolving from various conference presentations– Blog:http

://www.metaforsoftware.com/category/anomaly-detection-101/

– Many briefly mentioned issues, never explored– Needed more details and examples

• Note: 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!!

Wall of Charts™

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)

The fallacy of thresholds

• So what if my unicorn usage is at 89-91%, and has been stable?• I’d much rather know if it’s at 60% and has been rapidly

increasing

• Static thresholds and rules won’t help you in this case

Work smarter not harder

• We don’t need more metrics• We don’t need more thresholds and rules• We DO need better, smarter tools

TO THE RESCUE: Anomaly Detection!!

• Anomaly detection (also known as outlier detection) is the search for items or events which do not conform to an expected pattern. [Chandola, V.; Banerjee, A.; Kumar, V. (2009). "Anomaly detection: A survey". ACM Computing Surveys 41 (3): 1]

• For devops: Need to know when one or more of our metrics is going wonky

#monitoringsucks vs #imonitoring

• Proper monitoring tools should give us all the information we need to be PROACTIVE– But they don’t

• Current monitoring tools assume that the underlying system is relatively static– Surround it with static thresholds and rules.– Good for detecting catastrophic events but not

much else– WHY!!??

“Traditional” analytics …

• Roots in manufacturing process QC

… are based on Gaussian distributions

• Make assumptions about probability distributions and process behaviour– Usually assume data is normally distributed

with a useful and usable mean and standard deviation

What’s normal!!??

THIS is normal

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

Aaahhhh

• The mysterious red lines explained

The four horsemen

• Four horsemen of the modelpocalypse™ [Abe Stanway & Jon Cowie http://www.slideshare.net/jonlives/bring-the-noise]

– Seasonality– Spike influence– Normality– Parameters

Moving Averages for detecting outliers

• 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

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

Exponential Smoothing

• 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

• ALL assume Gaussian!

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

• BUT…• Cue in DRAMATIC MUSIC

What’s my distribution?

Another common distribution

Let’s look at an example

3-sigma rule

Holt-Winters predictions

Histogram – probability distribution

Another example

3-sigma rule

Holt-Winters predictions

Are we doomed?

• No!• There are lots of other non-Gaussian based

techniques:– Adaptive Mixture of Gaussians– Non-parametric techniques (

http://www.metaforsoftware.com/everything-you-should-know-about-anomaly-detection-know-your-data-parametric-or-non-parametric/)

– Spectral analysis

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

KS test with bootstrap

What about slow trends?

KS test on slow memory leak

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.

• A large amount of data center data is non-Gaussian– Guassian statistics won’t work– Use appropriate techniques

Pet Peeve: How much data do we need?

• Trend towards higher and higher sampling rates in data collection

• Reminds me of Jorge Luis Borges’ story about Funes the Memorious– Perfect recollection of the slightest details of every

instant of his life, but lost the ability for abstraction

• Our brain works on abstraction– We notice patterns BECAUSE we can abstract

The danger of over-abstraction

= comfortable?

So, how much data DO you need?

• You don’t need more resolution that twice your highest frequency (Nyquist-Shanon sampling theorem)

• Most of the algorithms for analytics will smooth, average, filter, and pre-process the data.

• Watch out for correlated metrics (e.g. used vs. available memory)

Think: Is all data important to collect?

• Two camps:– Data is data, let’s collect and analyze everything and

figure out the trends. – Not all data is important, so let’s figure out what’s

important first and understand the underlying model so we don’t waste resources on the rest.

• Similar to the very public bun fight between Noam Chomsky and Peter Norvig– http://norvig.com/chomsky.html

• Unresolved as far as I know

Shout out to etsy

• Check out kale:• Check out kale for some analytics:

– http://codeascraft.com/2013/06/11/introducing-kale/

– https://github.com/etsy/skyline/blob/master/src/analyzer/algorithms.py

• Only scratched the surface• I want to talk more about algorithms, analytics,

current issues, etc, in more depth, but time’s up!!– Go back in time to me Office Hours session, or– Come talk to me or email me if interested.

• Thank you!

toufic@metaforsoftware.com@tboubez