The document discusses the scaling of codelabs using Docker's Swarm mode and Docker Compose, presenting a training framework for hands-on learning experiences. It details the setup of virtual machines and container workloads as well as challenges and benefits of using orchestration solutions. The talk emphasizes the ease of operation, cost efficiency, and practical outcomes of using this method for training sessions.

1. Tales of
Training: Scaling
CodeLabs with
Swarm Mode
and Compose
Damien DUPORTAL
Training Engineer - CloudBees
@DamienDuportal - github.com/dduportal

2. Agenda
What is this talk about?
● Trench Story
● Drinking champagne
● Training / CodeLabs
● VM workload
● Container workload
● Orchestration
What is this talk NOT about?
● Toolings wars:
○ Kubernetes vs. Swarm
○ AWS vs. Azure
● Showing code (20 min only!)
○ Help yourself (GitHub):
http://bit.ly/2yoYG0v

3. Drinking my own champagne
Learn by TRYING:
● Experimentation never wastes
time
● Build something that works
and iterate

4. “CodeLab”?
● Part of a training (learning experience)
● Practical Exercise
● Using an isolated environment
● Dedicated to a single user
● Synonym of “workshop” or
“hands on lab”

5. CodeLab == Production
Constraints:
● Network
● Configuration diversity
● User Error (Learning process)
● Time boxed Sessions

6. CodeLab VM Content:
● Jenkins Training:
○ Store Git SCM (Gitea - Go)
○ Jenkins Master + Build Agents (JVMs)
○ Web Command Line (TTYDs - Python)
○ WebIDE (Codiad - PHP)
○ Store Artifacts (Artifactory - JVM)
○ Docker Registry (Go)

7. Challenges
● CodeLab defined by service list
○ Docker-compose
● Portability
○ Docker as blueprint
● Explicit definition
○ Definition as code

8. Step 1: VMs
Consul Servers
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
AWS EC2 VM
Host OS: Ubuntu
Docker & Compose
CodeLab Service Stack
Consul
SSH Bastion
Reverse Proxies
Register into
Consul
End User
Trainer
Deploys with
Terraform
Load-Balancer
Reconfigure with
Consul-Template

9. Step 1: Take Aways
It works!
Easy to begin with and to operate
Cost (1 attendee per VM)
TTP (Time To Play)

10. Road to Native Orchestration
● Solving Costs Issue: More attendees per VM
● Solving TTP Issue:
○ Moving to container-based workload
(Look at PWD speed!)
○ Un-tie Infra from Services

11. Step 2.1: Provide Infra
SSH Bastion“Infra
Admin”
Deploys with
Terraform
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)

12. Step 2.2: Provide Services
SSH Bastion“Infra
Admin”
Deploys with
Docker
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Deploys with
Docker
Trainer
CodeLab Service Stack
Support Services
Support Services

13. Step 2.3: Benefits
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Worker (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
Swarm Manager (EC2 VM)
CodeLab Service Stack
Support Services
Support Services
End User

14. Step 2: Positive Side Effect
● Relying on Docker ecosystem
● Less side-kicks to maintain

15. Step 2: Nice problems to have
● CodeLab Scaling Pattern
○ (Deploy N times, NOT Scaling)
● Monitoring / Metrics
● Resource Management

16. CodeLab Scaling Pattern
● Compose and Swarm:
○ Define a stack of services
○ Scale each horizontally
● Concern here:
○ Deploy N times the same stack

17. Monitoring/Metrics
It is NOT a challenge
● Should have done it at Step 1
● Feedback loop
● A LOT of “Working Out
of the box” tools

18. Resource Management
● Resource sharing patterns:
○ VM workloads: 1 stack for 1 VM resources
○ Container workload: N stacks for
N VMs resources
● JVM pattern: Capping Memory (OOM)
● Need to measure and iterate
○ Automation

19. Step 2: Take Aways
● It still works!
● Easier to begin with and to operate
● Cost: 4x less (4 attendees per VM)
● TTP: ~10 s instead of ~10 min
● Maintaining: 5 tools instead of 8

20. Thank you and enjoy DockerCon!