The Big Questions

1
The Big Questions
Life death
Future of the planet
Nature of the universe
Consciousness
2
How Do We Answer Them?
lt0
1700
1950
1990
3
The Same is True of Smaller Questions

• Designing new chemical catalysts
• Selling advertising
• Creating entertainment
• Finding parking

4
Information Technology and Science

• Paul Erdös claimed that a mathematician is a
  machine for turning coffee into theorems. The
  scientist is arguably a machine for turning data
  into insight.
• Service-Oriented Science, I. Foster, Science,
  308, p. 814.

5
What are the Products of Science?

• Papers
• We learned this, and heres how.
• Data and Datasets
• We collected this data. Download it, write an
  analysis program, and see what you can learn from
  it.
• Web Portals
• We constructed this scientific model. Use our
  data or bring your own, supply some parameters,
  and see how it behaves.
• Requires manual operation.
• Web Services
• Heres our climate model. Integrate it with your
  models for ocean currents/weather/crop
  forecasts and see what happens.
• Heres our indexed data from the latest
  experiment run. Run your filters against it and
  see if you can find anything interesting.
• Heres our genome analysis engine. Upload your
  proteins and see what they will do in a cell.

Increasing degrees of collaboration
6
GridAn Enabler of eScience
The dubious electrical power grid analogy

• Must we buy (or travel to) a power source?

Or can we ship power to where we want to work?
Enable on-demand access to, and integration
of, diverse resources services, regardless of
location
7
1st Generation Grids
Focus on aggregation of many resources for
massively (data-)parallel applications
EGEE
8
Second-Generation Grids

• Empower many more users by enabling on-demand
  access to services
• Science gateways (TeraGrid)
• Service oriented science
• Or, Science 2.0

Service-Oriented Science, Science, 2005
9
Web 2.0

• Software as services
• Data- computation-richnetwork services
• Services as platforms
• Easy composition of services to create new
  capabilities (mashups)that themselves may be
  made accessible as new services
• Enabled by massive infrastructure buildout
• Google projected to spend 1.5B on computers,
  networks, and real estate in 2006
• Many others are spending substantially
• Paid for by advertising

Declan Butler, Nature
10
Automating Science

• Human access to data is nice
• Automated access by software tools is
  revolutionary
• In the time that a human user takes to locate
  one useful piece of information within a Web
  site, a program may access and integrate data
  from many sources and identify relationships that
  a human might never discover unaided. - Foster

11
Science 2.0E.g., Virtual Observatories
Gateway
Data Archives
Figure S. G. Djorgovski
12
Service-Oriented Science

• People create services (data or functions)
• which I discover ( decide whether to use)
• compose to create a new function ...
• then publish as a new service.
• ? I find someone else to host services, so I
  dont have to become an expert in operating
  services computers!
• ? I hope that this someone else can manage
  security, reliability, scalability,

!
!
Service-Oriented Science, Science, 2005
13
Are Scientists Really Developing Web Services?
14
Cancer Bioinformatics Grid

• Common system architecture (caGrid) provides the
  service interface plumbing and the service
  hosting capability
• Web services
• Community participants supply useful services
• Data access, analysis, modeling, filtering,
  authoring, etc.
• https//cabig.nci.nih.gov/

15
The Introduce Authoring Tool

• Define service
• Create skeleton
• Discover types
• Add operations
• Configure security
• Modify service

Generates GT4-compatible WebServices
Introduce Hastings, Saltz, et al., Ohio State
University
16
The Importance of Hostingand Management
Tell me about this star
Tell me about these 20K stars
Support 1000sof users
E.g., Sloan DigitalSky Survey, 10 TB others
much bigger
17
Skyserver Sessions(Thanks to Alex Szalay)
18
Hosting ManagementApplication Hosting Services
Appln Code
Application providers
Application deployment
Application Prep Tool(s)
Provisioning
Application client
Resource Provider
Appln Code
Users
Resource Provider
Appln Code
AHSmanagement
Hosting Service
Author ization
Admins
Persistence
Policymanagement
PDP
19
Who Will Host Your Services?

• Your institution (campus resources)
• (Inter)national systems
• TeraGrid, Open Science Grid, UK Natl Grid
  Service, ChinaGrid, NaukaGrid, etc.
• Science domain systems
• caBIG, NEES, Earth System Grid, Orion, LEAD,
  NEON, LHC Computing Grid, etc.
• Commercial systems
• Amazon, Google, etc.

20
Examples ofProduction Scientific Grids

• APAC (Australia)
• China Grid
• China National Grid
• DGrid (Germany)
• EGEE
• NAREGI (Japan)
• Open Science Grid
• Taiwan Grid
• TeraGrid
• ThaiGrid
• UK Natl Grid Service

21
Application-Infrastructure Gap

• Dynamicand/orDistributedApplications

22
Bridging the Application-Resource Gap
User Application
Database
Specialized resource
Computers
Storage
23
Grid Infrastructure

• Distributed management
• Of physical resources
• Of software services
• Of communities and their policies
• Unified treatment
• Build on Web services framework
• Use WS-RF, WS-Notification (or WS-Transfer/Man)
  to represent/access state
• Common management abstractions interfaces