Monitoring “unknown unknowns” With Machine Intelligence

@guyfig

On-Call Engineer by Nature

"If a tree falls in a forest and no one is

around to hear it, does it make a sound?"

Observability is a superset between

monitoring and instrumentation.

Making systems debuggable and

understandable

@mipsytipsy

Do you really know what to observe?

Instrumentation - mostly Developer driven

What is the output? Dashboard? Exploration tool?

one can determine the behavior of the

entire system from the system's outputs

Observability In Control Theory

Unknown Unknowns - Rumsfeld Quadrant

-Static thresholds-Defined Alerts-Static Runbooks

-Anomaly Detection-Predictions-External Knowledge

-Knowledge-Recommendations-Auto Collaboration

-Inference-Auto Correlations-Semantic Analysis-Decision making

The Observability Quadrant (Based on Johari window)

Humans Driven Detection

Set thresholds to find patterns

Simulate based on known

Use percentiles, basic stats

Will That Help in a “Fire-Fighting” Mode?

Find The Problem

Thresholds? Baseline? Anomaly?

- Scale matters

- Stationary noise matters

- Use Autocorrelation

Preprocessing Data

sklearn.preprocessing

Independent component analysis (ICA)

separates a multivariate signal into

additive subcomponents that are

maximally independent.

from sklearn.decomposition

import FastICA, PCA

Find The Problem

CPU

90%

Time in Minutes EC2 Instance

changed from

t2.small to m3.xl

Events & context matters

Anomly?

Use Enrichment

What Can Machines Do?

Process different types of data, transform it fast and handle huge amounts in real-time

Automate and adapt Anomaly Detection

Apply Semantic text similarities to find patterns (Information Retrieval)

Apply auto correlation models

Evolve and adapt (overtime) based on human interaction

The Goal - Centralization

Observability for systems with imperfect outputs

Events enrichments, symptoms detection and inference

Automatic Outlier Detection

Automatic Correlation

Get closer to the Control Theory mathematical definition

Pick The Right Tools

https://github.com/turi-code/SFrame

- Define the model. Use a single schema (Apache Avro)

- Events are agnostic. Can represent logs, stack trace, metric, user action, HTTP event,

etc.

- Every event should have a set of common fields as well as optional key/value

attributes

Get a Common SchemaUse Common Schema

Deterministic models are better to start with (Fuzzy Logic, Rules)

Choose your logic and start run it across your data (schema)

Apply similarity checks to strings first (TF-IDF, BM25, Fuzzy, other classifiers)

Look into correlations, start with simple obvious ones, before building classifiers

(Unsupervised/Semi-supervised learning is much more relevant overall)

Build your prediction models on time series data first. (Statistics has solid models)

Time and context are dimensions you will be able to start addressing

Best Practices

Use It In Production

- Your team == your users

- Ask for feedback

- Re-calculate relevancy

- Apply Recommendations

based on your own team

knowledge

github.com/signifai

@guyfig

Thank You