This document summarizes the migration of a web application called TABLEAPP from AWS to GCP. It describes the original AWS architecture, problems encountered like slow scaling, and goals for the migration like improving performance and reducing costs. It then details experiments with Docker containers and Kubernetes on GCP and AWS. The selected solution deployed Kubernetes on GCP's Container Engine for auto-scaling and easy management. The new GCP architecture integrated Kubernetes, Cloud SQL, Cloud Storage and other services. This resulted in faster deployment times, higher performance, better log collection and a 40% reduction in costs compared to the original AWS architecture.

1. About me
● 陳彥文 / wenchen
● 自由開發者
● AWS五年經驗 / GCP一年經驗
● 過去在新創圈打滾

2. Migrate From AWS to GCP
TABLEAPP Architecture Story
Yen-Wen Chen
5 July 2017 GCPUG.TW

3. 緣由

4. 找出問題
TABLEAPP
Cloudfront CDN
Assets / Images
S3
DNS
Route53
Mail Server
SES
Notification
SNS
Clusters
EC2
Database
RDS / HA
1. 展開完整架構
2. 列出遭遇到的狀況
3. 盡可能開啟所有Log
4. 分析狀況來源

5. 選定改善目標
改善反應速率
資料庫效能
Log整合
降低部署時間
支撐更多的流量
降低成本
跨區負載

6. 選定改善目標及檢驗標準
改善反應速率
資料庫效能
Log整合
降低部署時間
支撐更多的流量
降低成本
跨區負載
● 資源使用效率
● 減緩月支出成長速度

7. 實驗

8. TABLEAPP AWS Architecture
TABLEAPP
Cloudfront CDN
Assets / Images
S3
DNS
Route53
Mail Server
SES
Notification
SNS
Load Balancer
Application
Clusters
EC2 / AutoScale
Database
RDS / HA

9. AWS Architecture Resource Problem
Scaling!!
● Auto Scaling Benefit
○ Maintain availability
○ Maintain performance
○ Decrease capacity during lulls to
reduce costs
● Monthly avg CPU usage under 5%
● EC2 requiring is slow
○ about 1~2min to activate a ec2
○ about 1~2min to update latest source
code
○ about 30s to run health check

10. Our solution is Container

11. Virturalized Infrastructure
Infrastructure
Host OS
Docker Engine
BINS/LIBS
APP
Container
BINS/LIBS
APP
Container
BINS/LIBS
APP
Container
We can ignore resource problem

12. Container Auto Scaling
Container A
Container B
Container C
Container A
Container B
Container C
Container A
Container C
Machine 1 Machine 2 Machine 3
If Container C causes CPU overload
Then put a new C into Machine 3

13. Service Auto Discovery
Container A
Container B
Container C
Container A
Container B
Container C
Container A
Container C
Machine 1 Machine 2 Machine 3
Service A Service B Service C
Service auto discovery / configuration / load balance

14. Other Benefits of Docker Moby
● Version Control
○ push code and build
○ every commit has its own images
● Lightweight
○ fast activation (~ 10s) and rollback
○ small image
● Isolation
○ applications and resources are isolated and segregated
● Protable
○ Multi-cloud platform support
○ BOSS asked that

15. We want to run moby on …?

16. Kubernetes vs EC2 Container Service
Kubernetes EC2 Container Service
Open Source NO
PaaS Google Compute Engine,
Rackspace, Microsoft Azure,
vSphere, ...
AWS
Community x
Management Kubectl / API AWS CLI / API
Protable Run anywhere AWS only
Service / Discovery built-in Elastic Load Balance

17. How about K8s on AWS?

18. Deploy Kubernetes on EC2
● Maintain Kubernetes yourself
○ Install
○ Testing
○ Update
○ and ….
● Handle auto-scale manually
○ remove pods before remove ec2
● Using CI to deploy image
● Set up logging and monitoring policy
● Integrate AWS resource and Kubernetes manually
○ It must be scalable
No, it sucks ...

19. Kubernetes on Google Cloud is awesome

20. Google Container Engine (GKE)
● Easy
● Full managed
○ even update kubernetes
● Logging and Monitoring support (stackdrive)
● Automatic and configurable cluster scaling
● Google Cloud Platform resource integration

21. Design new Architecture

22. TABLEAPP GCP Architecture
Cloud Load
Balancing
Database
Cloud SQL
EastAsia Cluster
Container Engine
TABLEAPP
Cloud CDN
TABLEAPP
Cloudfront CDN
Assets / Images
S3
DNS
Route53
Mail Server
SES
Notification
SNS

23. Architecture inside kubernetes
TABLEAPP Kubernetes Cluster
Front Service
API Service
Manage Service
Front
Front
Manage
Manage
API
CloudSQL Proxy
API
CloudSQL Proxy
API
Cloud Load Balancing
Manage
Cloud Load Balancing
WWW
Cloud Load Balancing
Database
Cloud SQL
Logger Service Logger
Logging

24. Deployment
EastAsia Cluster
Container Engine
BitBucket Build Trigger
Container Registry
Build Images
Container Registry
Base Images
Code Repository
ImageUpdate
Pub/Sub
Pods Update
Cloud Functions
Config Files
Cloud Storage
2
1 Base Images Dockerfile Updated
2 Web Source Code Updated
3 Build Images Process
4 Pull Latest Config File
5 Trigger Image Update
6 Notified Image Update
7 Pull Latest Docker Image
8 Pull Latest Config File From Pods
startup script
9 Migrate Database From Pods startup
script
Database
Cloud SQL
1
3
4 7
8
5
6
9

25. Log Collection
EastAsia Cluster
Container Engine
BitBucket Build Trigger
Container Registry
Build Images
Container Registry
Code Repository
Base Images
ImageUpdate
Pub/Sub
Pods Update
Cloud Functions
Config Files
Cloud Storage
Database
Cloud SQL
Cloud Load
Balancing
Tableapp
Cloud CDN
Logging
Build Log
Log Files
Cloud StorageBackup

26. Cost and Usage

27. Request History
Requests has grown
5 times in past year5x

28. Payment History
● cost increased
almost 2 times in six
month
● We start optimize
AWS architecture at
2016-11
Start
Optimize
2x

29. Payment History
● After reduce cost
● We start maintain
architecture in
Google at 2017-02
Start Test
Google

30. Payment History
Now
We redece cost successful
We can afford more request

31. Conclusion
● We still remain some service in AWS
○ SES
○ SNS
○ S3
○ Read replica Database
● CloudCDN is really fast
○ very low latency
○ optimize network
○ POP is stable
● Lower price
○ based on new arch, we save 40% cost

32. QA