Application Monitoring in a Post-Server World: Why Data Context is Critical

Confidential ©2008-15 New Relic, Inc. All rights reserved. 2

The Decline of the Server

Containerization Docker Amazon ECS

Zero config infrastructure-less compute AWS Lambda

©2008-15 New Relic, Inc. All rights reserved.

Lessons learned from Docker


Docker is the app’s lightweight VM

Long livedShort lived

VM

AmazonEC2



Well, that was surprising

49ACCOUNTS USING DOCKER

IN LAST 24 HOURS

9,974CONTAINERS REPORTING IN

IN LAST 24 HOURS



Apparent usage


AmazonEC2

VM



Along came New Relic Synthetics…

Test external availability and performance User authored selenium scripts run in our data

center Each run in its own container for security

isolation Most run for under a minute



Disposable compute container


VM

AmazonEC2

AWSLambd

a


Confidential ©2008-15 New Relic, Inc. All rights reserved.

What the heck’s going on?

Long livedShort lived? ? ?

VM

AmazonEC2

AWSLambd

a



We’re Data Nerds!


Docker container age, count vs. hours

100

10K

1M

3.7 M

83 days 333 days



100

10K

1M

3.7 M

VM’ish




100

10K

1M

3.7 M

EC2’ish




100

10K

1M

3.7 M

Lambda’ish



Container age, by hour under 24 hours

3,741,00046% under one hour



Container age, by minute under an hour

950,00011% under one minute



Container age, by minute under an hour

27% under 5 minutes (versus a VM?)



A surprising result


VMVM

AmazonEC2

AWSLambd

a



June versus now: 5x data, same shape


The evolution of computation as a serviceShort startup time (orders mag.) allows very short lived computing

Containers only exist, and only for as long, as they provide value.

Full stop.

Containers are created Do their work Go away


Elements of monitoring computation as a service

A mere list of instances doesn’t scale, nor help

De-provisioned containers still contribute to knowledge

Raw metrics just table stakes

Context matters to identify computational intent


Monitoring servers


Monitoring computation



Low friction install

New Relic AWS EC2 Beta addresses

Managing the dynamic nature of AWS

Managing the scale of AWS


Increased visibility

Application Monitoring

Server Monitoring

EC2 AWS Metadata


SaaS to SaaS monitoring, under one minute


Dynamic management

Provides AWS status

Maintains accurate EC2 list

Detects blind spots

Shows instance state


Context via tags and metadata as labels

AWS metadata

AWS custom

tags


EC2 instance name

AWS metadata

Custom label

Customer example


New Relic by instance typeSelected

labelInstance

countHealth status


New Relic by availability zone


New Relic by instance type in us-east-1



Future requirements for monitoring toolsThe obvious ones

Should handle scale

Should handle dynamic lifecycle

of resources

The less obvious ones

It’s a big data problem

Ops needs ways of quickly pivoting and

drilling in

Deeper understanding requires analytics (raw metrics not

enough) ©2008-15 New Relic, Inc. All rights reserved.

Kevin McGuire

We all love… … and yet

Common AWS scenarios

No large infrastructure build out

Quickly provision

Scale out to meet demand

Am I under-provisioned?

Am I over-provisioned?

How well am I responding to demand?


Prototype examples of Analytics

Application and Server metrics

Enriched with context from AWS

As New Relic Insights events


Detecting under-provisioned

Hmm, that’s bad

The smoking gun Aha!


Detecting over-provisionedHmm, that’s too

good

Aha!That’s a waste


Evaluating availability elasticity

Through scale outBut never

de-provisioned

Response time settles back

Increased load



The Ultimate AWS control panel

RESOURCES CUSTOMER EXPERIENCE COST


newrelic.com/aws

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Demo videosCome by the booth!

Date post:	18-Feb-2017
Category:	Data & Analytics
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Application Monitoring in a Post-Server World: Why Data Context is Critical

Data & Analytics