隨著時代的演進，管理機器的方式也隨著慢慢進化。隨著一台台機器的管理方式，變成了集群（Cluster）的管理方式． 而應用程式的部屬方始也從最原始的實體機部署，到了虛擬機器（VM）甚至到了容器（Container）的部署方式。 Kubernetes, DCOS 與 Docker Swarm 就是結合了容器部署與機器集群的管理方式的一個管理套件。但是集群的部屬方式並不代表你可以節省部署與維護的人力，因為隨著集群的數量變大，緊接來著就是需要更多管理的技巧。 比如說： 如何有效地管理集群的使用率? 如何讓集群內閒置機器能夠自動關閉? 如何有效地自動擴展你的集群? 如何讓集群偵測惡意攻擊或是自我修復? 講者將會帶來一個新的觀念，就是一個具有機器智能的集群管理系統。結合時下最受歡迎的機器學習架構（SMACK）與深度學習代表的 Tensorflow 如何讓你的集群能夠更有效地分析使用量，進而讓你的集群變得有智能。變成下一代的機器智能集群（Machine Intelligent Cluster）。

1. Machine Intelligent Cluster:
The next generation of data center
Evan Lin @Linker Networks

2. About me
Cloud Architect @ Linker
Networks
Golang User Group - Co-
Organizer
Top 5 Taiwan Golang open
source contributor (github
award)
Developer, Curator, Blogger

3. Recap Cloud Summit 2016

4. Agenda
• Problems on data center
• How machine learning helps
• Machine Intelligent Cluster
• Applications
• Q&A

5. Data center

6. • Power consumption
• Low usage
• Unpredictable peak
• Noisy neighbors
Efficiency
• Physical damage
• Networking problem
• Anomaly
• Attack
Risk
Real data center

7. Power consumption

8. Low usage and Unpredictable peak

9. Noisy neighbor

10. Use machine learning improve DC power
consumption

11. None of your business?

12. Modern Data center: Machine Cluster

13. Before machine cluster
DB Master:
IP: 192.168.1.222
DB Slave:
IP: 192.168.1.223
Web Server 1:
IP: 192.168.1.101
Web Server 2:
IP: 192.168.1.102
Web Server 3:
IP: 192.168.1.103
Load Balancer:
IP: 1.2.3.4

14. Container orchestration
Resource arrangement
Scalability
Portability
Automation migration

15. Resource management
3 Web App Servers
2 DB Servers
1 Load Balancer

16. Scalability

17. Automation migration

18. Automation migration

19. Automation migration

20. Automation migration

21. But .. we need better ..

22. No prediction

23. How to define scale out threshold?
50 %?
75 %?
25 %?

24. Machine Intelligent Cluster

25. Efficiency
Maximize
Utilization
Operation
Optimization
Accident
Risk
Mitigation
Serviceability
Management
Machine
Intelligence
Cluster
How MIC helps

26. Operation Optimization
1. Reinforcement learning
2. Adjust thermostat
3. Check the reward (CPU performance).
[1]: Refer from https://goo.gl/ly3zyX

27. Maximize Utilization
Analyze utilization and reduce working
machines to save our customer budget
- Predict utilization trend
- Provide auto-scaling threshold
adjustment

28. Prediction and dynamic threshold

29. Optimized
Scheduler
Node 1 Node 2
Node 3
Node 1 Node 2 Node 3
Nginx
(CPU 30%)
DB- MySQL
(IO 25%)
DB- Mongo
(IO 30%)
Apache
(CPU 30%)
Backend Process
(CPU 35%)
DB- Oracle
(IO 35%)
NodeJS
(CPU 7%)
Go backend
(CPU 8%) Nginx
(CPU 30%)
DB- MySQL
(IO 25%)
NodeJS
(CPU 7%)
Go backend
(CPU 8%)
Apache
(CPU 30%)
Backend Process
(CPU 35%)
DB- Mongo
(IO 30%)
DB- Oracle
(IO 35%)
Maximize Utilization
P.S. Not rearrange processes, we change the scheduler to avoid it happen..

30. Model 1
Serial Number Prediction
S.M.A.R.T. RNN Prediction
Serviceability Management (cont.)
Model 2

31. Dummy VM Detection Outlier Attack Detection
Mitigate risk

32. Storage SDN
Zombie Tagging system

33. Architecture

34. Cloud Native Architecture

35. HPC (with GPU) Server
Storage SDN
Storage SDN
Data Collect Probe & Sensor & Smart GW
Visualization
Data Process
Data Analysis &
Machine Learning
DCOS/
Kubernetes
Spark ML Tensorflow
DCOS / Kubernetes
Cassandra (Storage)
Kafka (Queueing)
Go/Akka (Connector)
Spark (ETL/Streaming)
D3.js
Scikit Learn R
Interactive
Dashboard
Jupyter Notebook
Zeppelin
ML Job
Scheduler
Chronos
MIC System Architecture

36. Data Agent Kafka
Spark
Streaming
Cassandra
Spark ML
(Classification,
Clustering)
TensorFlow
(Deep
Learning)
Backend Server
API
Portal
TensorFlow
Predict
SparkML Predict
MIC Data Flow

37. Applications on MIC
Machine Intelligent Cluster
IOT Gaming 5G NFV E-Commerce

38. Machine Intelligent Cluster Summary
• Machine cluster with Intelligent
• Features
• Self-Optimization
• Self-Learning
• Self-Recovery
• Green, Secure and Predictive machine cluster

39. 歡迎訂閱 碼天狗
http://weekly.codetengu.com/

40. Thank You