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The Grid, Grid Services and the Semantic Web: Technologies and Opportunities. Dr. Carl Kesselman Director Center for Grid Technologies Information Sciences Institute University of Southern California. Outline. What are Grids? Grid technology Globus and the Open Grid Services Architecture

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the grid grid services and the semantic web technologies and opportunities

The Grid, Grid Services and the Semantic Web: Technologies and Opportunities

Dr. Carl Kesselman

Director

Center for Grid Technologies

Information Sciences Institute

University of Southern California

outline
Outline
  • What are Grids?
  • Grid technology
    • Globus and the Open Grid Services Architecture
  • Grids and the Semantic Web
how do we solve problems
How do we solve problems?
  • Communities committed to common goals
    • Virtual organizations
  • Teams with heterogeneous members & capabilities
  • Distributed geographically and politically
    • No location/organization possesses all required skills and resources
  • Adapt as a function of the situation
    • Adjust membership, reallocate responsibilities, renegotiate resources
the grid vision
The Grid Vision

“Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations”

  • On-demand, ubiquitous access to computing, data, and services
  • New capabilities constructed dynamically and transparently from distributed services

“When the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances” (George Gilder)

biomedical informatics research network birn
Biomedical InformaticsResearch Network (BIRN)
  • Evolving reference set of brains provides essential data for developing therapies for neurological disorders (multiple sclerosis, Alzheimer’s, etc.).
  • Today
    • One lab, small patient base
    • 4 TB collection
  • Tomorrow
    • 10s of collaborating labs
    • Larger population sample
    • 400 TB data collection: more brains, higher resolution
    • Multiple scale data integration and analysis
national virtual observatory
National Virtual Observatory

http://virtualsky.org/

from

Caltech CACR

Caltech Astronomy

Microsoft Research

Virtual Sky has

140,000,000 tiles

140 Gbyte

Change scale

Change theme

Optical (DPOSS)

Xray (ROSAT) theme

Coma cluster

living in an exponential world 1 computing sensors
Living in an Exponential World(1) Computing & Sensors

Moore’s Law: transistor count doubles each 18 months

Magnetohydro-

dynamics

star formation

living in an exponential world 2 storage
Living in an Exponential World:(2) Storage
  • Storage density doubles every 12 months
  • Dramatic growth in online data (1 petabyte = 1000 terabyte = 1,000,000 gigabyte)
    • 2000 ~0.5 petabyte
    • 2005 ~10 petabytes
    • 2010 ~100 petabytes
    • 2015 ~1000 petabytes?
  • Transforming entire disciplines in physical and, increasingly, biological sciences; humanities next?
an exponential world 3 networks or coefficients matter
An Exponential World: (3) Networks(Or, Coefficients Matter …)
  • Network vs. computer performance
    • Computer speed doubles every 18 months
    • Network speed doubles every 9 months
    • Difference = order of magnitude per 5 years
  • 1986 to 2000
    • Computers: x 500
    • Networks: x 340,000
  • 2001 to 2010
    • Computers: x 60
    • Networks: x 4000

Moore’s Law vs. storage improvements vs. optical improvements. Graph from Scientific American (Jan-2001) by Cleo Vilett, source Vined Khoslan, Kleiner, Caufield and Perkins.

the grid world current status
The Grid World: Current Status
  • Dozens of major Grid projects in scientific & technical computing/research & education
  • Considerable consensus on key concepts and technologies
    • Open source Globus Toolkit™ a de facto standard for major protocols & services
    • Far from complete or perfect, but out there, evolving rapidly, and large tool/user base
  • Industrial interest emerging rapidly
  • Opportunity: convergence of eScience and eBusiness requirements & technologies
the next step
The Next Step
  • Globus leverages standard protocols
    • TLS, LDAP, X.509, HTTP
    • Only TCP in common
  • Is there a better foundation for Grid functions
    • More unified protocol stack (common base)
    • Better support for virtualization
    • Leverage commodity infrastructure
web services
“Web Services”
  • Increasingly popular standards-based framework for accessing network applications
    • W3C standardization; Microsoft, IBM, Sun, others
  • WSDL: Web Services Description Language
    • Interface Definition Language for Web services
  • SOAP: Simple Object Access Protocol
    • XML-based RPC protocol; common WSDL target
  • WS-Inspection
    • Conventions for locating service descriptions
  • UDDI: Universal Desc., Discovery, & Integration
    • Directory for Web services
transient service instances
Transient Service Instances
  • “Web services” address discovery & invocation of persistent services
    • Interface to persistent state of entire enterprise
  • In Grids, must also support transient service instances, created/destroyed dynamically
    • Interfaces to the states of distributed activities
    • E.g. workflow, video conf., dist. data analysis
  • Significant implications for how services are managed, named, discovered, and used
    • In fact, much of our work is concerned with the management of service instances
ogsa design principles
OGSA Design Principles
  • Service orientation to virtualize resources
    • Everything is a service
  • From Web services
    • Standard interface definition mechanisms: multiple protocol bindings, local/remote transparency
  • From Grids
    • Service semantics, reliability and security models
    • Lifecycle management, discovery, other services
  • Multiple “hosting environments”
    • C, J2EE, .NET, …
the grid service interfaces service data
The Grid Service =Interfaces + Service Data

Reliable invocation

Authentication

Service data access

Explicit destruction

Soft-state lifetime

Notification

Authorization

Service creation

Service registry

Manageability

Concurrency

GridService

… other interfaces …

Service

data

element

Service

data

element

Service

data

element

Implementation

Hosting environment/runtime

(“C”, J2EE, .NET, …)

given a set of services
Given a set of Services?
  • How do we do a better job of finding out what services we want to use
  • How do we do a better job of configuring services
  • How do we do a better job of composing and nesting services
  • Answer: Do a better job of representing services
deeper representation of services
Deeper representation of services
  • Information is captured via structure
    • X.509 certificates, MDS models, CIM schema, Metadata
  • Knowledge expresses relationships between entities
    • Concepts and relationships
    • Logical framework to inference over relationships
vision
Vision

“The Semantic Web is an extension of the current Web in which information is given a well-defined meaning, better enabling computers and people to work in cooperation. It is the idea of having data on the Web defined and linked in a way that it can be used for more effective discovery, automation, integration and reuse across various applications. The Web can reach its full potential if it becomes a place where data can be processed by automated tools as well as people”

From the W3C Semantic Web Activity statement

ontologies everywhere
Ontologies Everywhere
  • What happens if knowledge permeates the Grid
    • Data elements
    • Service descriptions (service data elements)
    • Protocols (e.g. policy, provisioning)
  • More dynamic and general model then Semantic Web
    • OGSA lifetime model
    • OGSA SDE model
cognative grid
Cognative Grid
  • Grid Services + Ontologies + Knowledge Driven Services
  • Examples
    • Knowledge driven matchmaking
    • Agent based service composition
    • High-level planning and resource discovery
    • Knowledge based provisioning
  • Some people are using term “semantic grid” to discribe Grid Services+Knowlege
scec modeling environment
SCEC Modeling Environment

KNOWLEDGE REPRESENTATION & REASONING

Knowledge Server

Knowledge base access, Inference

Translation Services

Syntactic & semantic translation

Knowledge Base

Ontologies

Curated taxonomies,

Relations & constraints

Pathway Models

Pathway templates,

Models of simulation codes

DIGITAL

LIBRARIES

Navigation &

Queries

Versioning,

Topic maps

Mediated

Collections

Federated

access

KNOWLEDGE

ACQUISITION

Acquisition Interfaces

Dialog planning,

Pathway construction

strategies

Pathway Assembly

Template instantiation,

Resource selection,

Constraint checking

Code

Repositories

FSM

RDM

AWM

SRM

Users

Data Collections

Data & Simulation

Products

GRID

Pathway Execution

Policy, Data ingest, Repository access

Grid Services

Compute & storage management, Security

Pathway Instantiations

Storage

Computing

docker publishing sha code
DOCKER: Publishing SHA Code

Web

Browser

User specifies:

  • Types of model parameters
  • Format of input messages
  • Documentation
  • Constraints

AS97

DOCKER

Model

Specification

User

Interface

AS97

docs

types

msg

constrs

Wrapper

Generation

(WSDL, PWL)

Constraint

Acquisition

AS97

ontology

SCEC

ontologies

(Y. Gil, USC/ISI)

recommends other models
Recommends other models

Yes

Did you know that [Sadigh97] is a good model for dist >80 miles?

mygrid project bioinformatics
myGrid Project - bioinformatics
  • Imminent ‘deluge’ of genomics data
    • Highly heterogeneous, Highly complex and inter-related
  • Convergence of data and literature archives
    • Database access from the Grid
    • Process enactment on the Grid
    • Personalisation services
    • Metadata services

Grid Services + Ontologies

Carol Gobel, U. Manchester

resource selection matchmaking
Resource selection: Matchmaking
  • Providers and requesters describe themselves
    • Synactic description
      • Structured or Semi-structured
  • A Matchmaker matches compatible classads
    • Match based on attribute name, simple prioritization
  • Semantic matchmaking
    • Inference based matching (e.g. CIM+relations)
    • Automatic classification (e.g. description logic)
    • Leverage domain specific ontologies
pegasus planning for execution in grids
Pegasus: Planning for Execution in Grids
  • Create workflow to create virtual data
    • Domain specific and generic rules
  • Map Workflow unto Grid resources
    • System state via Grid services (MDS, RLS,…)
    • Global and local optimization criteria
summary
Summary
  • Technology exponentials are changing the shape of scientific investigation & knowledge
    • More computing, even more data, yet more networking
  • The Grid: Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations
  • Many potential opportunities for application of semantic web technologies to Grid services
    • OGSA
partial acknowledgements
Partial Acknowledgements
  • Open Grid Services Architecture design
    • Karl Czajkowski @ USC/ISI
    • Ian Foster, Steve Tuecke @ANL
    • Jeff Nick, Steve Graham, Jeff Frey @ IBM
  • Semantic/Cognitive Grid
    • Yolanda Gil, Ewa Deelman, Jim Blythe, Tom Russ, Hans Chalupsky
    • Conversations with Jim Hendler, Carol Gobel, David DeRoure
  • Strong links with many EU, UK, US Grid projects
  • Support from DOE, NASA, NSF, Microsoft
for more information
Grid Book

www.mkp.com/grids

The Globus Project™

www.globus.org

OGSA

www.globus.org/ogsa

Global Grid Forum

www.gridforum.org

For More Information