An Introduction to Cloud-based
Services
Paul Watson
Newcastle University, UK
paul.watson@ncl.ac.uk
• e.g. Amazon
2
Plan
•
•
•
•
What is Cloud Computing?
Potential Advantages
Lessons from our own experiences
Cloud Issues
What is Cloud Computing?
“.. a broad array of
web-based services aimed at
allowing users to obtain a wide range of functional
capabilities
on a ‘pay-as-you-go’ basis
that previously required tremendous hardware/software
investments
and professional skills to acquire.”
Irving Wladawsky Berger
What’s New?
• illusion of Infinite computing resources On Demand
• no up-front commitment by users
• Pay for use of resources on a short-term basis as needed
(from “Above the Clouds: A Berkeley View of Cloud Computing”)
Example – Amazon Web Services
• Based on Xen VMs
– run any OS & software stack
• CPU: 1.0Ghz x86 instance
• Blob Storage
• External Data Transfer
@ $0.10 /hour
@ $0.12 /GB month
@ $0.10 /GB
• Also queue, key store, block store, range of instances
Why is this Important (I): Internal IT Problems
(slide by permission of Arjuna Technologies)
Dynamic Business
Demand
Silos =
Inflexibility
New
demand
Extinct
demand
Over-provision
Under-provision
Capacity
Capacity
Resources
Resources
Demand
Demand
Time
Time
7
Static IT Supply
Why is this Important (II)? Time to put Ideas into action
Research
1. Have good idea
2. Write proposal
3. Wait 6 months
4. If successful..
5. Buy Computers
6. Install Computers
7. Start Work
Science Start-ups
1. Have good idea
2. Write Business Plan
3. Ask VCs to fund
If successful..
4. Buy computers
5. Install Computers
6. Start Work
Why is this a Good idea: using commercial clouds
1.
2.
3.
4.
Have good idea
Grab nodes as needed from Cloud provider
Start Work
Pay for what you used
Cloud Services Continuum (based on Robert Anderson)
http://et.cairene.net/2008/07/03/cloud-services-continuum/
Salesforce.com
Google AppEngine
Platform
(PaaS)
Microsoft Azure
Amazon EC2 & S3
Infrastructure
(IaaS)
Complexity
Software
(SaaS)
Flexibility
Google Docs
Example Lessons from CARMEN Project
• Design began in 2006
– Commercial clouds not an option
• Designed own “private” cloud
• Experimenting with Commercial Cloud
CARMEN Project
UK EPSRC e-Science Pilot
£4M (2006-10)
20 Investigators
Stirling
St. Andrews
Newcastle
Manchester
York
Sheffield
Leicester
Warwick
Cambridge
Plymouth
Imperial
Industry & Associates
Research Challenge
Understanding the brain is the greatest
informatics challenge
• Enormous implications for science:
• Medicine
• Biology
• Computer Science
Collecting the Evidence
100,000 neuroscientists generate huge quantities of data
–
–
–
–
molecular (genomic/proteomic)
neurophysiological (time-series activity)
anatomical (spatial)
behavioural
Epilepsy Exemplar
Data analysis guides surgeon during operation
Further analysis provides evidence
WARNING!
The next 2 Slides show an exposed human brain
CARMEN
enables sharing and
collaborative exploitation of
data, analysis code and
expertise that are not
physically collocated
CARMEN e-Science Requirements
• Store
– very large quantities of data (100TB+)
• Analyse
– suite of neuroinformatics services
– support data intensive analysis
• Automate
– workflow
• Share
– under user-control
Background: North East Regional e-Science Centre
• 25 Research Projects across many domains:
• Bioinformatics, Ageing & Health, Neuroscience, Chemical
Engineering, Transport, Geomatics, Video Archives, Artistic
Performance Analysis, Computer Performance Analysis,....
• Same key needs:
Share
Automate
Analyse
Store
Result: e-Science Central
• Integrated Store-Analyse-Automate-Share infrastructure
• Generic
– CARMEN neuroinformatics & chemistry as pilots
e-Science Central
•Web based
•Works anywhere
e-Science
Central
Software as a
Service
• Dynamic Resource
Allocation
• Pay-as-you-Go*
Social
Networking
• Controlled Sharing
• Collaboration
• Communities
Cloud
Computing
Science Cloud Architecture
Access over
Internet
(typically via
browser)
Upload
data &
services
Run
analyses
Data storage
and
analysis
Science Cloud Options
Users
Science
App n
Science
App 1
Service Developers
Science
App 1
....
Science
App n
....
Science Platform
Cloud Infrastructure:
Storage & Compute
Cloud Infrastructure:
Storage & Compute
App
....
App
App API
e-Science Central
Security
Analysis Services
Social
Networking
Workflow
Enactment
Processing
Storage
Science Cloud
Platform
Cloud
Infrastructure
Editing and Running a Workflow on the Web
Workflow
Result File
Viewing the output of Workflow Runs
Viewing results
Blogs and links
Communicating Results
Linking to
results & workflows
What we learnt: Moving into a Cloud
• Moving existing technologies into a cloud can be difficult
– some can’t run in a Cloud at all
Raw Data Exploration with Signal Data Explorer
What we learnt : Scalability
• Clouds offer the potential for scalability
– grab compute power only when needed
• Developers have to manage scalability
– for Infrastructure as a Service Clouds
– scale up as well as down
Adaptive Dynamic Deployment
with Dynasoar
Commercial
“pay-as-you-go”
Response time (seconds)
450
400
Response time
(Seconds)
16
350
processors in pool
14
300
12
250
10
200
Adding Processors
as you need
them optimises
150 resources and
saves money100
in pay-as-you-go
clouds
8
6
4
50
2
Arrival Rate (messages per second)
1
1
1
0.5
0.5
0.5
0.25
0.25
0.13
0.13
Ensure system can also release
unwanted nodes
0.13
0.06
0.06
0.03
0.03
0
0.03
0
Processors in pool
clouds would allow us to avoid this
18
limit
Microsoft Azure Cloud for e-Science Demo
• Recent experiments with Microsoft Azure Cloud
– running Chemical analyses
– Silverlight App
Thanks to:
- Paul Appleby & Team at the Microsoft Technology Centre, Reading
- & MS External Research e-Science Group
Microsoft Azure Cloud Demo
When not to use Clouds?
• Large data transfers
–Time & Cost
• High Performance
– cpu/io/network bandwidth/low latency
• Predictable Performance
• Confidentiality
• High Availability?
• High Server Utilisation?
–private clouds better?
Create Private Cloud
(slides by permission of Arjuna Technologies)
Dynamic Business
Demand
New
demand
Arjuna AGILITY
Resources
Capacity
Resources
Demand
Capacity
Demand
Time
Time
Agile IT Supply
37
Private Cloud Examples
• Eucalyptus
– Amazon API
• Private Cloud deployments of Microsoft Azure
• Arjuna Agility
Federating Private & Public Clouds
Public
Cloud
Public
Cloud
e.g. Amazon
App1
Arjuna Agility
App1
App1 & 2
Service
Agreement
Internal Cloud
Dept A
Dept B
39
Public Cloud
e.g. Amazon
App1
App1
Public Cloud
e.g. FlexiScale
Arjuna Agility
App1
App1 & 2
40
Internal Cloud
Arjuna
Dept A
Dept B
Summary
• Cloud computing can revolutionise e-science
– provide sustainable infrastructure
– reduce time from idea to realisation
• Don’t underestimate complexity
– building scalable distributed systems is still hard
– can Science Clouds help by lowering the hurdles?
• e-Science Central
– Store-Analyse-Automate-Share e-science platform
– adding content from a range of domains
– CARMEN is evaluating it for neuroinformatics