Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
MOM! My algorithms SUCK
Search
Abe Stanway
September 19, 2013
Programming
15
2.8k
MOM! My algorithms SUCK
Given at Monitorama.eu 2013 in Berlin.
http://vimeo.com/75183236
Abe Stanway
September 19, 2013
Tweet
Share
More Decks by Abe Stanway
See All by Abe Stanway
Building Data Driven Organizations
astanway
1
210
A Deep Dive into Monitoring with Skyline
astanway
6
1.8k
Bring the Noise: Continuously Deploying Under a Hailstorm of Metrics
astanway
34
8k
Data Visualization in the Trenches
astanway
5
710
Gifs as Language
astanway
2
840
Your API is a Product
astanway
3
980
Zen and the Art of Writing Commit Logs
astanway
3
820
Other Decks in Programming
See All in Programming
コーディングは技術者(エンジニア)の嗜みでして / Learning the System Development Mindset from Rock Lady
mackey0225
2
580
Dart 参戦!!静的型付き言語界の隠れた実力者
kno3a87
0
210
The Past, Present, and Future of Enterprise Java
ivargrimstad
0
170
tool ディレクティブを導入してみた感想
sgash708
1
150
画像コンペでのベースラインモデルの育て方
tattaka
3
1.9k
MCPで実現するAIエージェント駆動のNext.jsアプリデバッグ手法
nyatinte
6
860
マイコンでもRustのtestがしたい その2/KernelVM Tokyo 18
tnishinaga
2
2.3k
AI OCR API on Lambdaを Datadogで可視化してみた
nealle
0
180
GitHub Copilotの全体像と活用のヒント AI駆動開発の最初の一歩
74th
8
3.2k
令和最新版手のひらコンピュータ
koba789
14
8k
Kiroの仕様駆動開発から見えてきたAIコーディングとの正しい付き合い方
clshinji
1
150
CSC305 Summer Lecture 06
javiergs
PRO
0
100
Featured
See All Featured
Java REST API Framework Comparison - PWX 2021
mraible
33
8.8k
jQuery: Nuts, Bolts and Bling
dougneiner
64
7.9k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
248
1.3M
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
36
2.5k
Building a Scalable Design System with Sketch
lauravandoore
462
33k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
8
890
Stop Working from a Prison Cell
hatefulcrawdad
271
21k
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
33
2.4k
The Art of Delivering Value - GDevCon NA Keynote
reverentgeek
15
1.6k
Navigating Team Friction
lara
189
15k
The Language of Interfaces
destraynor
160
25k
Typedesign – Prime Four
hannesfritz
42
2.8k
Transcript
@abestanway MOM! my algorithms SUCK
i know how to fix monitoring once and for all.
a real human physically staring at a single metric 24/7
that human will then alert a sleeping engineer when her
metric does something weird
Boom. Perfect Monitoring™.
this works because humans are excellent visual pattern matchers* *there
are, of course, many advanced statistical applications where signal cannot be determined from noise just by looking at the data.
can we teach software to be as good at simple
anomaly detection as humans are?
let’s explore.
anomalies = not “normal”
humans can tell what “normal” is by just looking at
a timeseries.
“if a datapoint is not within reasonable bounds, more or
less, of what usually happens, it’s an anomaly” the human definition:
there are real statistics that describe what we mentally approximate
None
“what usually happens” the mean
“more or less” the standard deviation
“reasonable bounds” 3σ
so, in math speak, a metric is anomalous if the
absolute value of latest datapoint is over three standard deviations above the mean
we have essentially derived statistical process control.
pioneered in the 1920s. heavily used in industrial engineering for
quality control on assembly lines.
traditional control charts specification limits
grounded in exchangeability past = future
needs to be stationary
produced by independent random variables, with well- defined expected values
this allows for statistical inference
in other words, you need good lookin’ timeseries for this
to work.
normal distribution: a more concise definition of good lookin’ μ
34.1% 13.6% 2.1% 34.1% 13.6% μ - σ 2.1%
if you’ve got a normal distribution, chances are you’ve got
an exchangeable, stationary series produced by independent random variables
99.7% fall under 3σ
μ 34.1% 13.6% 2.1% 34.1% 13.6% 2.1% μ - σ
if your datapoint is in here, it’s an anomaly.
when only .3% lie above 3σ...
...you get a high signal to noise ratio...
...where “signal” indicates a fundmental state change, as opposed to
a random, improbable variation.
a fundamental state change in the process means a different
probability distribution function that describes the process
determining when probability distribution function shifts have occurred, as early
as possible. anomaly detection:
μ 1
μ 1 a new PDF that describes a new process
drilling holes sawing boards forging steel
snapped drill bit teeth missing on table saw steel, like,
melted
processes with well planned expected values that only suffer small,
random deviances when working properly...
...and massive “deviances”, aka, probability function shifts, when working improperly.
the bad news:
server infrastructures aren’t like assembly lines
systems are active participants in their own design
processes don’t have well defined expected values
they aren’t produced by genuinely independent random variables.
large variance does not necessarily indicate poor quality
they have seasonality
skewed distributions! less than 99.73% of all values lie within
3σ, so breaching 3σ is not necessarily bad 3σ possibly normal range
the dirty secret: using SPC-based algorithms results in lots and
lots of false positives, and probably lots of false negatives as well
no way to retroactively find the false negatives short of
combing with human eyes!
how do we combat this?* *warning! ideas!
we could always use custom fit models...
...after all, as long as the *errors* from the model
are normally distributed, we can use 3σ
Parameters are cool! a pretty decent forecast based on an
artisanal handcrafted model
but fitting models is hard, even by hand.
possible to implement a class of ML algorithms that determine
models based on distribution of errors, using Q-Q plots
Q-Q plots can also be used to determine if the
PDF has changed, although hard to do with limited sample size
consenus: throw lots of different models at a series, hope
it all shakes out.
[yes] [yes] [no] [no] [yes] [yes] = anomaly!
of course, if your models are all SPC-based, this doesn’t
really get you anywhere
use exponentially weighted moving averages to adapt faster
fourier transforms to detect seasonality
second order anomalies: is the series “anomalously anomalous”?
...this is all very hard.
so, we can either change what we expect of monitoring...
...and treat it as a way of building noisy situational
awareness, not absolute directives (alerts)...
...or we can change what we expect out of engineering...
...and construct strict specifications and expected values of all metrics.
neither are going to happen.
so we have to crack this algorithm nut.
...ugh. @abestanway