The adoption of microservices add a new layer of complexity to an already complex application environment. When application issues arise, it becomes difficult to pinpoint the source and, too often, you find yourself in lengthy war rooms or assigning experts to triage every issue. As a result, brand loyalty, customer satisfaction and innovation are negatively impacted. The old approach to monitoring application performance just isn’t working. A new approach is needed. In this session we will discuss four areas that you should consider which will change the way you think about monitoring microservices

1. Four Considerations when
Monitoring Microservices
Amy Feldman
Director
CA APM Product Marketing
@amyfel
Jason Bloomberg
President, Intellyx
jason@intellyx.com
@theebizwizard

2. About Jason Bloomberg
• President of industry analyst firm
Intellyx
• Agile digital transformation thought
leader
• Write for Forbes, several blogs, biweekly
newsletter the Cortex
• Buy my latest book,
The Agile Architecture
Revolution
Copyright © 2015, Intellyx, LLC2

3. New Architecture for the
Application Economy
Copyright © 2015, Intellyx, LLC3
From Monolithic to Microservices
PhotoCredit:Centeractivehttp://www.centeractive.com/content/enterprise-service-bus
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4. What are Microservices?
Parsimonious, cohesive units of execution
– Designed to do one thing very well
– Contain everything from the operating system,
platform, framework, runtime and dependencies,
packaged as one unit of execution
Copyright © 2015, Intellyx, LLC4
PhotoCredit:JamesLeehttps://www.flickr.com/photos/jronaldlee/

5. Parsimony
• As small as they should
be but no smaller
• As part of refactoring,
ask:
– Would this microservice
work better if it were
smaller?
– Would this microservice
work better if it were split
into two or more
microservices?
Copyright © 2015, Intellyx, LLC5
PhotoCredit:AJChttps://www.flickr.com/photos/ajc1/

6. μservice
Cracking Open a
Microservice
• Shared nothing in the
sense that each
microservice has these
elements
• Each element is itself
parsimonious &
coherent
Copyright © 2015, Intellyx, LLC6
Code
Runtime
Cache
OS Elements
Service Interface

7. Microservices Externalize
Management
• Runtime code within
microservice pared down to
absolute minimum
– Not part of an ESB or other
managed environment
• Microservices know nothing
about other microservices
• May (or may not) run in a
container
– Containers need management
too!
Copyright © 2015, Intellyx, LLC7
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8. Microservice Monitoring
Challenges
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Complexity
Change
Scale
Resilience

9. Complexity
• The more
parsimonious and
coherent your
microservices, the
more of them you’ll
need
• Danger of
microservice-to-
microservice traffic
exploding
• Challenges of cache
consistency
Copyright © 2015, Intellyx, LLC9
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10. Change
• Microservices
empower broader,
more diverse DevOps
efforts
• Kanban-centric
continuous delivery
well-suited for
microservices
• New ones will be
popping up all the
time!
Copyright © 2015, Intellyx, LLC10
PhotoCredit:DennisHamiltonhttps://www.flickr.com/photos/orcmid/

11. Container
Code
Libraries
μservice
μservice
μservice
Container
Code
Libraries
μservice
μservice
μservice
Scale
• Any identical
microservice
instance may
respond to a
request
• Container-based
autoscaling
requires its own
management
Copyright © 2015, Intellyx, LLC11
Container
Code
Libraries
μservice
μservice
μservice
Container
Code
Libraries
μservice
μservice
μservice

12. Resilience
• Microservices
facilitate automated
recovery from failure
– Load balancing more
dynamic
– Cache refreshes
more complicated
– Composition logic
more labyrinthine
Copyright © 2015, Intellyx, LLC12
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13. Jason Bloomberg
President, Intellyx
jason@intellyx.com
@theebizwizard
Send email NOW to ca@intellyx.com to
download this presentation
Thank You!
Copyright © 2015, Intellyx, LLC

14. Addressing New Monitoring Challenges
Introduced by Microservices

15. 15 © 2015 CA. ALL RIGHTS RESERVED.
Microservices Introduces Four New Monitoring Challenges
Complexity Change
ScaleResiliency

16. 16 © 2015 CA. ALL RIGHTS RESERVED.
Desired Experience in Modern/Agile Environments
ANDREW CHEUNG
Application Developer
RYAN CAMPBELL
Production Support Analyst
PETE MILLER
Level 1 Support Analyst
MY PAIN
Monitoring many
systems & Devices
HELP ME
Simplify alerts, meaning
and action
MY PAIN
Bottleneck – work on
all issues
HELP ME
Understand app
performance
characteristics quickly
MY PAIN
Firefighting – not just
code changes
HELP ME
Instantly know what
changes trigger problems
MY PAIN
Administering many
Monitoring tools
HELP ME
Simplify administration
and support
new apps quickly
KYLE THOMAS
APM Administrator

17. 17 © 2015 CA. ALL RIGHTS RESERVED.
Microservices CloudContainers
Simply Will Not Scale
PERSPECTIVES
Complexity

18. 18 © 2015 CA. ALL RIGHTS RESERVED.
1 Application Topology: 3 PerspectivesCreate your own task-relevant
perspective for any APM user
APM Team Center – Role-Based Task Perspectives
The right context, for the right person, at the right time
MESSY TRUTH CUSTOM ATTRIBUTES AND FILTERS APPLIED

19. 19 © 2015 CA. ALL RIGHTS RESERVED.
Change
TIMELINES
Where do I
start?
Did my change
cause the
performance issue?
What
changed?
80% of Mean Time to
Resolution is finding
what changed
Lacks intuitive and
visual change
detection
Correlation of
changes to
performance
requires expertise
Tools are too
descriptive to
understand

20. 20 © 2015 CA. ALL RIGHTS RESERVED.
BEFORE CHANGE AFTER CHANGE
1: App flow change 3: Then performance
degrades
2: Two new services
were added
4: It began here!
APM Team Center – Timeline View
Quickly see changes in the context of performance problems
5: Context details
for next action

21. 21 © 2015 CA. ALL RIGHTS RESERVED.
Resiliency/Noise
BASELINES
 Maintaining alerts is time consuming,
even with automated baselines
 Difficult to visually determine where
to focus resources
 Reactive not proactive
 How red is red?
What issue should
I work on first?
Should I escalate?
Why is every
issue a fire drill?
How long has this
been an issue?
INCIDENT PANEL
24
CRIT
40
OTHER
308
OK
CRIT CPU CHK (member) Server Microsoft Windows 0h 48min
CRIT CPU CHK (member) Server Linux 3h 34min
CRIT CPU CHK (member) Server Linux 2h 13min
CRIT Disk Cap CHK Server Microsoft Windows 0h 48min
CRIT Disk Cap CHK Server Microsoft Windows 3h 34min
WARN CPU CHK (member) Server Linux 2h 13min
WARN Disk Cap CHK Server Linux 0h 48min
CRIT Disk Cap CHK Server Microsoft Windows 3h 34min
WARN CPU CHK (member) Server Linux 2h 13min
WARN Disk Cap CHK Server Linux 0h 48min

22. 22 © 2015 CA. ALL RIGHTS RESERVED.
“…one must separate the signal
from the noise within the data.”
-Walter Shewhart
Differential Analysis – Better Than Baselines
Fast, Easy, and Accurate Triage
Focus
attention here
Trending toward
an issue
Issue is getting
better
Not an issue
Issue is getting
better

23. 23 © 2015 CA. ALL RIGHTS RESERVED.
Scale
SmartInstrumentation
 Components come and go with
greater frequency
 Monitoring tools do not scale with
the application
 Too much data collected to make any
sense of it
API
Will my monitoring
environment scale to the
needs?
How do I understand the
relationship of all the
microservices?
What should be
monitored?

24. 24 © 2015 CA. ALL RIGHTS RESERVED.
E.P.I.C.APM
Elegance @Scale
Smart Instrumentation
•Collect what you need when you need it
Scale Monitoring
•Monitor @scale and clustering
Ease Administration
•Get control with CA APM ACC
Simply Visualize Large Complex Data
•CA APM Team Center Perspectives role-base task views
based on pivotable attributes

25. 25 © 2015 CA. ALL RIGHTS RESERVED.
Maintenance
Improvements
What’s new in CA APM 10.1
Team Center Map APIs & UX improvements
Browser Agent
Improvements
Node.js Agent
• EM on Java 8
• Domain changes with no restart
• .NET socket monitoring DNS issue fixed
• DA variance intensity expanded
• Thread-dump truncation fix
Simplification
Work Phase 1

26. 26 © 2015 CA. ALL RIGHTS RESERVED.
Support for Modern Development Technologies
New Node.js agent
Monitor apps deployed to
Docker containers with java
agent
Real time monitoring of
Application and Node.js
health/performance
Easily monitor apps deployed
using CF java build pack
Collects basic health metrics
from Pivotal CF (Field Pack)
Visibility into application
performance and CF Pivotal
health
Automatic collection of
deep dive diagnostic details
Easily install and deploy
agents without having to
modify the Node.js app source
code
Visualize app
performance and Docker
health in one view
Collects basic container
health metrics (Field Pack)

27. 27 © 2015 CA. ALL RIGHTS RESERVED.
Node.js Agent
Monitors apps written in node.js Automatically collects deep dive diagnostic
snapshots
Correlates performance to
application tiers
Does not require any modifications to the
Node.js app source code

28. 28 © 2015 CA. ALL RIGHTS RESERVED.
PROACTIVE INTELLIGENT COLLABORATIVEEASY
The Key to Stimulate User Loyalty and
Differentiate Your Business in the App Economy
Driving Toward
E.P.I.C. APM
Thank you