The document provides an overview of cloud computing, including definitions and concepts. It discusses NIST's definition of cloud computing and its key characteristics. It also covers cloud service models like IaaS, PaaS and SaaS, deployment models including private, public and hybrid clouds, and examples of cloud services and providers. The document then discusses data centers, components like nodes and networking, and operations concepts in cloud computing.
3. Cloud Computing
What is Cloud Computing from Youtube
Cloud Computing Explained from Youtube
The NIST Definition of Cloud Computing in PDF
什麼不是雲端運算 翟本喬 in PDF
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4. NIST definition
Cloud computing is a model for enabling
convenient, on-demand network access
to a shared pool configurable computing
resources.
On demand self service Rapid elasticity
Broad network access Measured service
Resource pooling
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6. Cloud Computing
Device Device Device Device Device Device
Apps Apps Apps
User Interface
Service Service Service Service Service Service
Software Software
Virtual Platform
Platform
Platform
Infrastructure
Data Center
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7. Cloud Computing
IaaS: Infrastructure as a Service
PaaS: Platform as a Service
SaaS: Software as a Service
XaaS: X as a Service
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8. Cloud Computing
Private Cloud
Community Cloud
Public Cloud
Hybrid Cloud
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9. Cloud Services
Amazon AWS/EC2
Google Services/Chrome OS
Dropbox
Apple iCloud
CHT HiCloud
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11. Data Center
Google Container Data Center Tour
Google Data Center in Street View
Dirty Cloud of Amazon, Apple, Microsoft
Dirty Cloud
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12. Traditional Data Center
• Different Hardware
• Mixed Hot/Cold Aisle
• Different Operation System
• Simple Policy of Networking
• Different Application Software
• Install and Boot from Local Disk Driver
• Single Point of Failure or Redundancy
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13. Cloud Data Center
• All Same Hardware
• All Same Operation System
• May run Software with Virtualization
• Complex Policy of Netowkring
• Boot from Network without Install
• Easy to Move And Quickly Service When Fail
• Easy to Extend for Scale
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14. Data Center
Power Efficiency Networking
Cooling Efficiency Facility Storage Space
Space Efficiency Easy Management
Computing Power Easy Maintenance
Data Storage Space Easy to Recovery
Bandwidth
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15. Power efficiency
Generator loss
Travel Line Loss
Transformer Loss
Circuit Loss
Green power, Solar, Wind, or Others
Power Recycle from Heat Water/Air
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16. Cooling Efficiency
Temperature
Humidity
Particle
Cooling Water
Room Temperature Water
Free Air
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17. Space
Arrange More Host in a Rack
Hot/Cool Aisle Separate
Weather Consideration
Location
Power/Pipeline/Network management
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18. Computing
Super Fast Single Computer
Parallel Computing in Single Computer
Distributed Computing in a Group
Virtualization in Single Computer
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20. Node Hardware
CPU Video
Multi CPU, Multi Core, Multi Threads None for save power consumption
RAM Network Interface
More RAM for More Performance More Bandwidth and Weak on LAN Support
Disk Cooling Fan
More for Aggregate Space None is better or at least need to be control
Serial Power Supply
For Easier Maintenance High Efficiency with Higher Voltage
BIOS
Easier Setting and Quickly Boot
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21. Node Software
Open Source is the First Choice
Cheap and easier to use
No Central Controller
Prevent complex HA design for single point of failure
No Specifically Hardware
Easier Management and Maintenance
No Dedicated Network Address Connect
More Scaleability
Services from Profile and Template
No manual inactive
Keep Simple of Software
Let Operating System take care as it can
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22. Monitoring
CPU/Memory Load
Networking Load
HDD
System Temperature
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23. Node
Simple Hardware
Complex Software
Auto Negotiation and Share Information
Between Nodes
Self Management
Easier Maintenance
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24. OS Boot Sequence
• BIOS POST
• Device Drive
• Boot Loader
• Kernel
• Load Device Driver
• Shell
• Application Service
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25. NetBoot With PXE
Power On
BIOS Hardware
• PXELinux Initialize
NetBoot?
• DHCP With Option 82
N
Y
PXE NetBoot
Local Boot
Requiire
• TFTP Load IP Address
Require
DHCP Server
• RootFS In RAM Disk
Boot Kernel TFTP Server Linux Kernel
RAM Require
RootFS Image
RAM DISK TFTP Server RootFS Image
Require
• Data Center Services System
Management
DNS Server
Maintenance
NTP Server
Init local HDD
• OS Level Initialize scripts
Join Distributed System
Virtualization
FileSystem Configuration
Distributed
FileSystem
Bring Up Cloud Cloud Apps
OS
Cloud OS up
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30. Storage
Access, Backup and Recovery
Network Attached Storage (NAS/SAN)
iSCSI/ATA over Ethernet
Network Share Storage
NFS, SMB, P2P
Distributed Storage
GlusterFS, MooseFS
Object Storage
OpenStack Swift, HDFS
List of File Systems
NFS/GlusterFS/MooseFS test
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31. Gluster FS
File Based Distributed File System
No Single Point of Failure
Distributed Meta-Data
Required Fully-Mash Connection with TCP
Still not Stable for Large Scale Access
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32. MooseFS
File based Distributed File System
High Loading in Mata-Data Server
Has Single Point of Failure
Seems more Stable
Not Easy to Expend
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33. OpenStack Swift
Object Storage
Slow with Python
Large Object Read/Write too Slow Issue
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34. HDFS
Object Storage
Slow with Java
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35. RDRB/NFS/iSCSI
Single Point of Failure
Connection Limitation
Bandwidth Limitation
Not Real Distributed File System
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36. BigData
You might Has Collected a Lot of Data First
What is your Data Means and Value
That is Application Stuff
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43. Virtualization
MS Windows
Large Scale
Easy to Recovery
Large Size of File as HDD Image
Complex Network Configuration
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44. Xen
Specific Kernel required
Not Really Open Source
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45. QEMU
Emulate x86/x86-64/PowerPC/ARM...
Emulated Hardware
Slow Speed
Single Thread
HyperVisor
Support Any Guest OS include MS Windows
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46. KVM
Only for Linux
Specific Kernel Required
Can work with QEMU
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47. LXC
Linux Host and Guest only
Chroot like not really isolated Host and Guest
Guest Speed as Host
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52. Next Step
We live in a connected world, and modern software has to
navigate this world. So the building blocks for tomorrow’s very
largest solutions are connected and massively parallel. It’s not
enough for code to be “STRONG and SILENT” any more. Code
has to talk to code. Code has to be chatty, sociable, well-
connected. Code has to run like the human brain, trillions of
individual neurons firing off messages to each other, a
massively parallel network with no central control, no single
point of failure, yet able to solve immensely difficult problems.
And it’s no accident that the future of code looks like the
human brain, because the endpoint of every network are, at
some level, human brains.
From ZeroMQ
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53. Next Step
如何讓一個人可以維護上萬部機器
如何讓程式像人一樣的互相配合、互相合作
自我檢視、自我維護、自我協調、故障排除
故障預警
無中央集權式的管理
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