The move towards microservices in Docker, EC2 and Lambda points to a shift towards shorter lived resources. These new application architectures are driving new agility and efficiency. But they, while providing developers with inherent scalability, elasticity, and flexibility, also present new challenges for application monitoring. The days of static server monitoring with a single health and status check are over. These days you need to know how your entire ecosystem of AWS EC2 instances are performing, especially since many of them are short lived and may only exist for a few minutes. With such transient ephemeral resources, there is no server to monitor; you need to understand performance along the lines of computational intent. And for this, you need the context in which these resources are performing. Join Kevin McGuire, Director of Engineering at New Relic, as he discusses trends in computing that we’ve gleaned from monitoring Docker and how they’ve helped us rethink how we monitor and analyze AWS. We’ll dive into the case for how contextual information like instance size, AMI, availability zone and tags can be used to drive an elevated understanding of transient infrastructure behavior and how it contributes to application performance. We’ll show how integrating status information gives you a more accurate view of EC2 lifecycle and health. And finally, how that information powers the ability for you to analyze and display that performance information in new and powerful ways.

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

3. The Decline of the Server
▪ Containerization
▪ Docker
▪ Amazon ECS
▪ Zero config infrastructure-less compute
▪ AWS Lambda
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4. Lessons learned from Docker
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5. Docker is the app’s lightweight VM
Long livedShort lived
VM
Amazon
EC2
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6. 6
Well, that was surprising
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49
ACCOUNTS USING DOCKER
IN LAST 24 HOURS
9,974
CONTAINERS REPORTING IN
IN LAST 24 HOURS
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7. 7
Apparent usage
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Long livedShort lived
Amazon
EC2
VM
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8. 8
Along came New Relic Synthetics…
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▪ 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
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9. 9
Disposable compute container
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Long livedShort lived
VM
Amazon
EC2
AWS
Lambda
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10. What the heck’s going on?
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Long livedShort lived
? ? ?
VM
Amazon
EC2
AWS
Lambda
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11. We’re Data Nerds!
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12. Docker container age, count vs. hours
10
0
10
K
1
M
3.7
M
83 days 333 days
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13. Docker container age, count vs. hours
10
0
10
K
1
M
3.7
M
VM’ish
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14. Docker container age, count vs. hours
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10
0
10
K
1
M
3.7
M
EC2’ish
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15. Docker container age, count vs. hours
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10
0
10
K
1
M
3.7
M
Lambda’ish
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16. Container age, by hour under 24 hours
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3,741,000
46% under one hour
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17. Container age, by minute under an hour
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950,000
11% under one minute
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18. Container age, by minute under an hour
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27% under 5 minutes
(versus a VM?)
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19. A surprising result
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Long livedShort lived
VMVM
Amazon
EC2
AWS
Lambda
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20. June versus now: 5x data, same shape
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21. The evolution of computation as a service
Short 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
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22. 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
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23. Monitoring servers
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24. Monitoring computation
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25. Low friction install
New Relic AWS EC2 Beta addresses
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Managing the dynamic
nature of AWS
Managing the scale
of AWS
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26. Increased visibility
Application Monitoring
Server Monitoring
EC2 AWS Metadata
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27. SaaS to SaaS monitoring, under one minute
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28. Dynamic management
Provides AWS status
Maintains accurate EC2 list
Detects blind spots
Shows instance state
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29. Context via tags and metadata as labels
AWS
metadata
AWS
custom tags
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30. EC2 instance name
AWS metadata
Custom label
Customer example
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31. New Relic by instance type
Selected label
Instance count
Health status
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32. New Relic by availability zone
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33. New Relic by instance type in us-east-1
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34. Confidential ©2008-15 New Relic, Inc. All rights reserved.
Future requirements for monitoring tools
The 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)
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35. 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?
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36. Prototype examples
of Analytics
Application and Server metrics
Enriched with context from AWS
As New Relic Insights events
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37. Detecting under-provisioned
Hmm, that’s bad
The smoking gun Aha!
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38. Detecting over-provisioned
Hmm, that’s too good
Aha!That’s a waste
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39. Evaluating availability elasticity
Through scale out
But never
de-provisioned
Response time
settles back
Increased load
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40. The Ultimate AWS control panel
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RESOURCES CUSTOMER EXPERIENCE COST
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41. newrelic.com/aws
Sign up for Beta notification
AWS Monitoring resources
Demo videos
Come by the booth!