Find out how to configure and package clustered Payara Micro with load balancing, automatic scaling and dedicated storage for building cloud-native microservices. Then with the help of cloud scripting and triggering, automate CI/CD for the deployed application and emulate the load to check the scaling and performance results.

1. Automated Scaling of Microservice Stacks
for JavaEE Applications

2. Why I am here?
I want to discuss with you:
• General autoscaling concept for JavaEE applications
• Real issues and Tricky things
My goals are:
• Save your time if you decide you need to scale your app
• Share our own experience

3. I am sure you’ve heard those words many times …
I just deploy my application to Docker, set a number of replicas and
… have it running … bla bla bla … super easy! Done!

4. Yes, pretty easy …
If you need a super scalable and clustered Hello World application …

5. In real life with JavaEE application
we have architecture similar to this
and its autoscaling becomes not a very trivial task…
In real life with JavaEE application we have architecture
similar to this

6. Theoretical Part

7. At least
applications scalable
At least 3 key things to make your
applications scalable

8. Adapted runtime
(Container + JVM + JavaEE Server)
micro
FIRST:

9. Preferably, thin versions of application servers
NO! YES!

10. There are various versions adapted
for lightweight containers
micro
GlassFish
There are various versions adapted for lightweight
containers

11. Any specific reason to use VMs for microservices?
NO!

12. Well…it might be, but …
Well…it might be, but …

13. One container - one microservice instance
NO!YES!

14. What should we use instead?
What should we use instead?
LXD
YES!

15. APP
MSMS
MS MS
Application
prepared for microservices
SECOND:

16. SORRY,
Application
prepared for SCALABLE microservices

17. Next few slides can be a bit boring for those
who know…
BUT
Still important!!!!!!
Next few slides can be a bit boring for those who know

18. horizontal
vertical
Scaling vectors

19. Decomposition into microservice
At least
Decomposition into microservice
At least 2 key approaches

20. Logical division

21. 1. Analyze metrics (NewRelic /
JavaVisualVM)
2. Find a weak point (!)
3. Move this part to microservice
4. Repeat
Splitting bigger parts into smaller

22. Orchestration
software
THIRD:

23. CONTAINERS
PROVISIONING
HEALTHCHECKS
METRICS
GATHERING
APPLICATION
LIFECYCLE
MANAGEMENT
AUTOMATIC
LOAD BALANCING
SERVICE
DISCOVERY &
CONNECTIVITY
SCALING
CUSTOM
SCRIPTING*
What it typically does?

24. CPU
HDD
(disk I/O)
SERVICE
FAILURE
RAM
NETWORK
SCHEDULER
Typical triggers that are used for scaling

25. Practical Part
Tricky things. Typical issues. Examples

26. horizontal
vertical
In which direction to scale?
Tricky thing #1

27. TOPOLOGY
Java EE Server
Business Tier
Web Tier
EJB App1 EJB App2
JSP App1
Java EE
Micro
Instance
JSP App1
Java EE
Micro
Instance
JSP App2
JSP App1
Java EE
Micro
Instance
JSP App1
Java EE
Micro
Instance
Scaling type
Java EE
Micro Instance
Java EE
Micro Instance
Example #1
JSP App2 JSP App2JSP App1
JSP App1
Java EE
Micro
Instance
JSP App1
Java EE
Micro
Instance
JSP App1
Java EE
Micro Instance
JSP App1
Java EE
Micro
Instance
JSP App1
Java EE
Micro
InstanceJava EE
Micro Instance
JSP App1 JSP App2
Legacy JavaEE application

28. Example #2
CPU NO
YES
YES
NO
NO
RAM
HDD (disk I/O)
NETWORK
SCHEDULER
Scaling type VERTICAL
Databases Triggers configuration

29. (dedicatee)
Takes much time to sync data
Temporary locks
Temporary performance degradation
RESUME
Hard to use auto horizontal scaling
Automatic vertical scaling can be used just fine
It’s better to keep spare instances underloaded
(dedicated storage per instance)
Typical issues while scaling of database instances

30. Events subscription
Usually, just spinning up an extra instance is NOT enough
Tricky thing #2

31. onAfterScaleOut[nodeGroup:ejb-app1] {
“call”: “registerNodeInMonitoringSystem”,
“call”: “addMemberToHazelcast”,
“call”: “call3rdPartyApiService”
}
onAfterScaleIn[nodeGroup:jsp-app2] {
“call”: “removeNodeFromMonitoringSystem”,
“call”: “removeMemberFromHazelcast”
}
Example #1
Typical ScaleIn/ScaleOut events and cases
THE FULL WORKING AND MORE COMPLEX CODE EXAMPLE CAN BE FOUND HERE:
https://github.com/jelastic-jps/payara/blob/master/payara-micro-cluster/manifest.jps

32. 1. Decrypt encrypted volume, that was attached on-fly
onAfterVolumeAttached[nodeGroup:instance] {{
“call”: “decryptVolume”
}
2. Get auth keys by instance before joining DAS
onBeforeStartService[nodeGroup:instance] {
“cmd”: “/root/scripts/pullAccessKeys.sh ${token}”
}
3. Deprovision node from DAS if cluster was shrinked
onAfterScaleOut[nodeGroup:das] {
“cmd”: “./asadmin_proxy remove ${instance[@last].ip}”
}
Example #2
THE FULL WORKING AND MORE COMPLEX CODE EXAMPLE CAN BE FOUND HERE:
https://github.com/jelastic-jps/glassfish/blob/master/manifest.jps

33. Proper triggers configuration
Tricky thing #3
This tuning is a long process, so be patient

34. Typical problem #1
Changing the value in one place can be reflected in other places

35. Load Testing
Detect deviations in testing loop
How to deal with this?

36. The triggers can become outdated with new code
Typical problem #2

37. COMMIT BUILD TESTING
PERFORMANCE
ANALYSIS
DELIVERY
The fix is easy here
Inject metrics collection as a part of your CI/CD step

38. Getting rid of spare things
Tricky thing #4

39. Some of JavaEE Servers are designed for VMs, not for containers
On example of Weblogic
Typical problem #1

40. Just remove everything spare
What is the right way to go?

41. Network is full of useless traffic
Total bandwidth
Useful traffic
Typical problem #2
Multicast | Heartbeats

42. Avoid multicast for members detection
Instance 1
Orchestrator
Instance 2
Instance 3
What we can do?
Use events and unicast

43. Real use case
On-Demand Scalability and Easy Management of Java EE Project: Hybrid Cloud for Miele
• Migrated from VMs to Containers
• Migrated from GlassFish to WildFly
• Improved scaling and HA
• Implemented Hybrid Cloud

44. Java in Containers
https://jelastic.com/java-cloud-hosting/

45. Jelastic.com
info@jelastic.com
@jelastic