Performance Monitoring for Docker Environments - Docker Amsterdam June Meetup

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Performance monitoring for Docker Challenges around Docker monitoring - Anomaly detection - CoScale demo For more info about how to use CoScale Docker monitoring, some reading material here: http://www.coscale.com/blog/how-to-monitor-docker-containers-with-coscale and http://www.coscale.com/blog/how-to-monitor-your-kubernetes-cluster A summary of CoScale Docker performance monitoring can be found here: http://www.coscale.com/docker-monitoring

Software

Performance Monitoring
for Docker environments
Anomaly detection
Monitoring tools
Live demo

About me
@coscale
www.coscale.com
@spolfliet
stijn.polfliet@coscale.com

Performance Monitoring
for Docker environments
Anomaly detection

Static alerts
TODO : more realistic business examples
!
!
!

?
seasonality
correlations
changing or dynamic
environment
Static alert limitations

Challenges
Machine learning perspective: unsupervised learning problem
? evaluation criteria ?

Challenges
statistical significance relevance
⇏

Holt-Winters
● seasonal exponential smoothing
● works quite well on ‘laboratory
data’
● calculation of prediction intervals
relies on normal distribution after
removal of seasonality
● => on our real world seasonal
data generates too many false
positives

Sliding window approach
model
evaluation
of new
data

Local outlier factor
Existing instance based machine learning technique (lazy,
~kNN)
Based on concept of local density
local outlier factor(A) =
density at point A
average density of kNN of point A
LOF >> 1 ⇒ outlier
en.wikipedia.org/wiki/Local_outlier_factor

Local outlier factor, no strong model assumption
spinning
process

Local outlier factor, no free lunch
Scaling: comparing apples and oranges
scale ⇒ distance ⇒ density ⇒ LOF-score
Autoscaling? (Mahalanobis distance) => enlarges
dimensions with low variance
“Curse of dimensionality”
dimensionality reduction preprocessing (e.g. PCA), but don’t throw
away the anomalies with the bathwater
Choosing cross-sections of data to analyze together, e.g.
different metric on same container
same metric on different containers

Load balance detector
Compare multiple signals (mean + variance) in load-balanced environment

Performance Monitoring
for Docker environments
Monitoring Docker

Orchestrator (services, volumes, replication controllers, …)
Resource usage (cpu, memory, disk, network, ...)
Container internals (application, database, caching, etc.)
Lightweight monitoring for lightweight microservices environment
What to monitor?

Docker Stats API
$ docker stats
CONTAINER CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O
1285939c1fd3 0.07% 796 KiB / 64 MiB 1.21% 788 B / 648 B 3.568 MB / 512 KB
9c76f7834ae2 0.07% 2.746 MiB / 64 MiB 4.29% 1.266 KB / 648 B 12.4 MB / 0 B
d1ea048f04e4 0.03% 4.583 MiB / 64 MiB 6.30% 2.854 KB / 648 B 27.7 MB / 0 B
Docker API

docker run
--volume=/:/rootfs:ro
--volume=/var/run:/var/run:rw
--volume=/sys:/sys:ro
--volume=/var/lib/docker/:/var/lib/docker:ro
--publish=8080:8080
--detach=true
--name=cadvisor
google/cadvisor:latest
open http://<your-hostname>:8080/
CAdvisor

agent runs in 1 container
container resource usage
no container internals
no application monitoring
15$ / month / server
Scout
scoutapp.com

agent runs in 1 container or on host
container resource usage
basic application monitoring
15$ / month / server
Datadog
datadoghq.com

kernel module captures system calls
container resource usage
basic application monitoring
20$ / month / server
Sysdig
sysdig.com

deep application monitoring
too heavyweight for lightweight container environments
100+$ / month / server
APM vendors

Lightweight agent
• Server metrics from OS
• Container and cluster metrics from Kubernetes and Docker APIs
• Application metrics from log files and management interfaces
• Business & custom metrics from various sources
Contextual events
• Container lifecycle
• Deployments & software releases
• Infrastructure changes
• Custom events
CoScale approach

Questions?
or contact me at stijn.
polfliet@coscale.com
@spolfliet

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