David De Roure

University of Southampton

www.semanticgrid.org

The Semantic Grid:
Past, Present and Future

A Semantic Grid Masterclass

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

Vision: J.C.R Licklider

“Lick had this concept of the intergalactic network which he believed was everybody could use computers anywhere and get at data anywhere in the world. He didn’t envision the number of computers we have today by any means, but he had the same concept – all of the stuff linked together throughout the world, that you can use a remote computer, get data from a remote computer, or use lots of computers in your job.”

Larry Roberts, in Segaller, S (1998). “Nerds: A brief
history of the internet”, New York, TV Books

Vision: Collaboratory

A collaboratory is

…a center without walls, in which the nation's researchers can perform their research without regard to geographical location, interacting with colleagues, accessing instrumentation, sharing data and computational resources, and accessing information in digital libraries

William Wulf, 1989

U.S. National Science Foundation

Vision: e-Science

e-Science is about global collaboration in key areas of science and the next generation of [computing] infrastructure that will enable it.

e-Science will change the dynamic of the way science is undertaken.

John Taylor, Director General of UK Research Councils

Vision: Grid

Grid computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation...we [define] the "Grid problem”…as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources - what we refer to as virtual organizations

From "The Anatomy of the Grid: Enabling Scalable Virtual Organizations" by Foster, Kesselman and Tuecke

Vision: The Grid

“The ongoing convergence between Grids, Web Services and the Semantic Web is a fundamental step towards the realisation of a common service-oriented architecture empowering people to create, provide, access and use a variety of intelligent services, anywhere, anytime, in a secure, cost-effective and trustworthy way.”

Next Generation Grids 2

Requirements and Options for

European Grids Research 2005-2010 and Beyond

EU Expert Group Report July 2004

Vision: The Grid

Courtesy of Ian Foster

Challenges: virtual orgs

Resource configurations are transient, dynamic and volatile as services (databases, sensors, compute servers) switched in and out

They are ad-hoc as service consortia have no central location or control, and no existing trust relationships

They may be large, with hundreds of services orchestrated at any time

They may be long-lived, for example a protein folding simulation could take weeks

Scale of data and compute resources is large

Quality of Service and performance criteria are severe

Platform must be scalable, able to evolve, fault-tolerant, robust, persistent and reliable

It should work seamlessly, and transparently – the user might not know or care where their calculation is done using how many machines, or where data is actually held

Registries organize

services of interest

to a community

R

R

R

R

Registries organize

services of interest

to a community

Registries organize

services of interest

to a community

Challenges: integration

Many sources

of data, services,

computation

Access

Data integration activities

may require access to, &

exploration of, data at

many locations

Exploration & analysis

may involve complex,
multi-step workflows

Security

service

Security

service

Policy

service

Policy

service

Security & policy

must underlie access

& management

decisions

Discovery

Access

Data integration activities

may require access to, &

exploration of, data at

many locations

Exploration & analysis

may involve complex,
multi-step workflows

RM

RM

RM

RM

RM

Resource management

is needed to ensure

progress & arbitrate

competing demands

RM

RM

RM

RM

RM

Resource management

is needed to ensure

progress & arbitrate

competing demands

Many sources

of data, services,

computation

Discovery

Courtesy of Ian Foster

Challenges: interop and reuse

myGrid

Combechem

Wish to reuse

Data

Services

Knowledge

Software

Anticipated use

Unanticipated use

Vision: The Grid

The Grid is fundamentally about joining things up, in an automated fashion, in order to do things that weren’t possible before

DDeR

Instruments

Services

People

Computation

Data

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

Two infrastructure enablers

On demand transparently constructed multi-organisational federations of distributed services

Distributed computing middleware

Computational Integration

An automatically processable, machine understandable web

Distributed knowledge and information management

Information integration

Grid

Computing

Semantic

Web

Grid Infrastructure

Container

System Services and Features

Handlers like WS-RM, Security, Programming Models like BPEL
or Registries like UDDI

Generally Useful Services and Features

Such as “Access a Database” or “Submit a Job” or “Manage
Cluster” or “Support a Portal” or “Collaborative Visualization”

Application Specific Services

such as “Run BLAST” or “Look at Houses for sale”

Clarify

these

areas

So we
can focus
here

Fox

Standard mechanisms for describing and invoking services: WS-I

Standard interfaces and behaviours for distributed systems: naming, service state, lifetime management, notification

Standard services: agreement, data access and integration, workflow, security, policy, brokering…

Specific services: drug discovery pipeline, sky surveys

Open Grid Service Architecture

WSRF Profile

for OGSA

Web Services

Grid Applications

Grid Services

Building systems by composition of heterogeneous components demands that we standardise common patterns

Approach to resource identification

Lifetime management interfaces

Inspection & monitoring interfaces

Base fault representation

Service and resource groups

Notification

More than basic Web Services!

OGSA Design Philosophy

(Enhanced) Service Oriented Architecture

Implicit Interface Extension (WSDL 2.0 ‘extends’)

Resources as First Class Entities (WSRF-Resources)

Data type extensibility and introspection

Dynamic service/resource/property creation and destruction

Use Case Driven

Big and Small Science plus Industry and Commerce

Component Based

Elements of the architecture are pluggable

Customizable

Support for dynamic, domain-specific content, ...

Within the same standardized framework

GRID

COMPUTING

UTILITY

COMPUTING

DISTRIBUTED

COMPUTING

Architecture Overview

Core Services

Base Profile

WS-Addressing

Privacy

WS-Base Notification

CIM/JSIM

WSRF-RAP

WSDM

WS-Security

Naming

OGSA-EMS

OGSA Self Mgmt

GFD-C.16

GGF-UR

Data Model

HTTP(S)/SOAP

Discovery

SAML/XACML

WSDL

WSRF-RL

Trust

WS-DAI

VO Management

Information

Distributed query processing

ASP

Data Centre

Use Cases &

Applications

Collaboration

Multi Media

Persistent Archive

Data Transport

WSRF-RP

X.509

GGF

Five Myths busted!

Isn’t it just for Physics?

No – e.g. Grids for Life Science and Medicine could dominate Grid applications

Think of the range and scale of data and the community

Isn’t it just High Performance computing?

No – it’s a generic mechanism for forming, managing and disbanding dynamic federations of services

Data integration, data access, data transport will dominate

Application integration is the key

Five Myths busted!

Isn’t it just a bag of protocols glued together?

No – the Open Grid Service Architecture gives a well specified middleware stack built on industry standard web services

Isn’t it just Globus toolkit?

No – that is one implementation

Isn’t it just a bunch of academics?

No – commercial vendors are making serious investment

GGF

Standards

Community

Operations

Leading the pervasive adoption of grid computing for research and industry

Defining grid specifications that lead to broadly adopted standards and interoperable software

Ensuring ongoing support of our mission and communication of our progress

Building a broad international community for the exchange of ideas, experiences, requirements, and best practices

GGF

The Grid ecosystem

Government

Commercial Vendor

Industry

Users

Other Consortia

Researchers

Academia

Computer Science

Research Labs

Government

Funding Programs

Government

Labs

Software /Hardware Vendors

Industry Solution Providers

Grid Publications

Globus

Internet2

GGF

IETF

Present / Future Users

Applied Science

OASIS

Academic

Labs

Media

Other Standards Orgs

Industry / Technology Publications

Analyst Publications

Communications Vendors

Service Providers

HPC Users

e.g.: Pharma

SOA Apps

e.g. Transactions

W3C

KEY

HPC = High Performance Computing

SOA = Services Oriented Architecture

Researchers

Economic Development Programs

National Publications

Partners

Online Publications / Bloggers

DMTF

Other Consortia

Grid Installs

Japan Grid

Korean Grid

European Grid

TerraGrid

Other Grid

Efforts

Grid Ecosystem

GGF

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

Origins of the Semantic Web

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.

W3C Activity Statement

Layers of Languages

You are here

Identity

Standard Syntax

Metadata annotations

Ontologies

Rules & Inference

Explanation

Attribution

Making Knowledge Explicit

RDF

DAML+OIL

OIL

OWL

All influenced by RDF

Ontology Inference Layer

DAML

OWL Lite (thesaurus)

OWL DL (reason-able)

OWL Full (anything goes)

OWL Web Ontology Language

RDF Resource Description Framework

Rocket Science (not)

Is this rocket science? Well, not really. The Semantic Web, like the World Wide Web, is just taking well established ideas, and making them work interoperably over the Internet. This is done with standards, which is what the World Wide Web Consortium is all about. We are not inventing relational models for data, or query systems or rule-based systems. We are just webizing them. We are just allowing them to work together in a decentralized system - without a human having to custom handcraft every connection.

Tim Berners-Lee, Business Case for the Semantic Web, http://www.w3.org/DesignIssues/Business

Web vs Semantic Web

<a href=

URI>

HTML

Web page

Any Web Resource

<a href=“http://…”>

RDF

URI

URI

URI

RDF is like the web!

<mind:Person rdf:id=“Hendler”>

<mind:title jobs:Professor>

<jobs:placeOfWork http://www.cs.umd.edu>

</mind:Person>

DOC1

Hendler

DOC1

Mind:title

Jobs:placeOfWork

Web Page

http://www…

Professor

Jobs:

Mind:

Jobs:

Sem Web models start from RDF…

N3

In RDF, information is simply a collection of statements, each with a subject, verb and object - and nothing else.

In N3, you can write an RDF triple just like that, with a period:

<#pat> <#knows> <#jo> .

Everything, be it subject, verb, or object, is identified with a Universal Resource Identifier. This is something like

<http://www.w3.org/> or <http://www.w3.org/2000/10/swap/test/s1.n3#includes>

Primer: Getting into RDF & Semantic Web using N3, W3C

Example

<#pat>	<#age> 24;	<#eyecolor> "blue" .

<#al>	<#age> 3;	<#eyecolor> "green" .

<#jo>	<#age> 5;	<#eyecolor> "green" .

green

5

Jo

green

3

Al

blue

24

Pat

Eyecolor

Age

The O-Word

An ontology is an explicit specification of a conceptualization [Gruber93]

An ontology is a shared understanding of some domain of interest. [Uschold, Gruninger96]

There are many definitions

a formal specification EXECUTABLE

of a conceptualization of a domain COMMUNITY

of some part of world that is of interest APPLICATION

Defines

A common vocabulary of terms

Some specification of the meaning of the terms

A shared understanding for people and machines

GGF5 Ontologies Tutorial

5 Myths Busted!

Isn’t it just AI and distributed agents (again)?

No – It is primarily metadata integration and querying

Don’t you need all that reasoning stuff?

No – A little bit of semantics goes a long way! (Hendler)

It only applies to the Web?

No – the technologies are being used for Enterprise integration, exposing data in a common model, common ontology languages, representing terminologies.

One big ontology of everything never works!

No – multiple ontologies; multiple everything!

One big Semantic Web!

No – lots of Semantic Web-lets, and expect it to break!

Compare and contrast

Er, … yet to be proven

Scalability and performance

Technology push: pioneers are primarily from the knowledge, agent and A.I. communities.

Application pull: pioneers are application scientists with large scale collaboration problems, originally computationally-oriented.

Information & knowledge is the new utility

Information & compute power as a utility

Information integration, based on metadata, ontologies and reasoning

Programmatic integration, originally based on protocols & toolkits

Distributed knowledge and information management

Distributed computing middleware

An automatically processable, machine understandable web

On demand transparently constructed multi-organisational federations of distributed services

The Semantic Web

The Grid

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

So what is the Semantic Grid?

The Grid vision is about large scale distributed collaboration – virtual organisations

Fundamentally, this means that information and knowledge in and about the system must be ‘machine processable’

The Semantic Grid is an extension of the current Grid in which information and services are given well-defined meaning, better enabling computers and people to work in cooperation

The full richness of the Grid ambition depends upon realising the Semantic Grid

The Semantic Grid Report 2001

At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality.

However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale.

www.semanticgrid.org

NB Report updated – March 2005 issue of Proceedings of the IEEE

Building bridges

Semantic Grid

Scale of data and computation

Scale of
Interoperability

Semantic
Web

Classical
Web

Semantic
Grid

Classical
Grid

Based on an idea by Norman Paton

Semantics in and on the Grid

The Semantic Grid is an extension of the current Grid in which information and services are given well-defined meaning, better enabling computers and people
to work in cooperation

Semantics in and on the Grid

Grid

Computing

The

Semantic

Web

The Semantic Grid

Web Services

Compute Resources

Catalogs

Data Archives

Information

Discovery

Metadata

delivery

Data

Discovery

Data

Delivery

Catalog Mediator

Data mediator

1. Portals and Workbenches

Bulk Data

Analysis

Catalog

Analysis

Metadata

View

Data

View

4.Grid

Security

Caching

Replication

Backup

Scheduling

2.Knowledge & Resource

Management

Standard Metadata format, Data model, Wire format

Catalog/Image Specific Access

Standard APIs and Protocols

Concept space

3.

5.

6.

7.

Derived Collections

Astronomy Sky Survey

Data Grid

Grid is metadata based middleware

Annotations of results, workflows and database entries could be represented by RDF graphs using controlled vocabularies described in RDF Schema and DAML+OIL

Personal notes can be XML documents annotated with metadata or RDF graphs linked to results or experimental plans

Exporting results as RDF makes them available to be reasoned over

RDF graphs can be the “glue” that associates all the components (literature, notes, code, databases, intermediate results, sketches, images, workflows, the person doing the experiment, the lab they are in, the final paper)

The provenance trails that keep a record of how a collection of services were orchestrated so they can be replicated or replayed, or act as evidence

For example…

At the data/computation layer: classification of computational and data resources, performance metrics, job control, management of physical and logical resources

At the information layer: schema integration, workflow descriptions, provenance trail

At the knowledge layer: problem solving selection, intelligent portals

Governance of the Grid, for example access rights to databases, personal profiles and security groupings

Charging infrastructure, computational economy, support for negotiation; e.g. through auction model

More…

Represent the syntactic data types of e-Science objects using XML Schema data types

Represent domain ontologies for the semantic mediation between database schema, an application’s inputs and outputs, and workflow work items

Represent domain ontologies and rules for parameters of machines or algorithms to reason over allowed configurations

Use reasoning over execution plans, workflows and other combinations of services to ensure the semantic validity of the composition

Use RDF as a common data model for merging results drawn from different resources or instruments

Capture the structure of messages that are exchanged between components

Yet more…

Goals

Many grid applications are set to benefit from semantic web tools and techniques. The semantic web includes standards and tools for immediate use (e.g RDF), ongoing activities (such as the W3C Web Ontology Working Group) and an active community of researchers. This RG provides a forum to track semantic web community activities, determine relevance to grid activities, provide a route for transfer of information and ideas between the communities and coordinate activities as appropriate.

GGF Semantic Grid RG

Motivating examples

"Correlate the new molecular structure with the existing structural databases; what are the likely physical properties of the crystal?"

"Retrieve & align 2000nt 5' from every serine/threonine kinase in Fabacae expressed exclusively in the root cortex whose expression increases 5x or more upon infection by Rhizobium but is not affected by osmotic or heavy-metal stresses & is <40% homologous in the active site to kinases known to be involved in cell-cycle regulation in any other species"

Wikipedia entry

The Semantic Grid refers to an approach to Grid computing in which information, computing resources and services are described in standard ways that can be processed by computer. This makes it easier for resources to be discovered and joined up automatically, which helps bring resources together to create virtual organizations. The descriptions constitute metadata and are typically represented using the technologies of the Semantic Web, such as the Resource Description Framework (RDF).

By analogy with the Semantic Web, the Semantic Grid can be defined as "an extension of the current Grid in which information and services are given well-defined meaning, better enabling computers and people to work in cooperation."

This notion of the Semantic Grid was first articulated in the context of e-Science, observing that such an approach is necessary to achieve a high degree of easy-to-use and seamless automation enabling flexible collaborations and computations on a global scale.

The use of Semantic Web and other knowledge technologies in Grid applications is sometimes described as the Knowledge Grid. Semantic Grid extends this by also applying these technologies within the Grid middleware.

Some Semantic Grid activities are coordinated through the Semantic Grid Research Group of the Global Grid Forum.

http://en.wikipedia.org/wiki/Semantic_Grid

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

Semantics in e-Science

Ontology-aided workflow construction

RDF-based semantic mark up of results, logs, notes, data entries

RDF-based service and data registries

RDF-based metadata for experimental components

RDF-based provenance graphs

OWL based controlled vocabularies for database content

OWL based integration

Engineering Design

BuddySpace

Compendium

I-X Process panels

Meeting Replay

BuddySpace

Compendium

I-X Process panels

Jabber Server

NASA Scenario

Compendium maps from trained compendium astronaut

Remote Science Team (RST) on earth e.g. geologists

Video and

Science Data

2. Virtual meeting of RST

using CoAKTinG tools

Plan for next

Day’s EVA

1. Astronauts debrief on EVA

Mars

Image from NASA

GGF9 Semantic Grid Workshop

The Role of Concepts in myGrid Carole Goble

Planning and Metadata on the Computational Grid Jim Blythe

Semantic support for Grid-Enabled Design Search in Engineering Simon Cox

Knowledge Discovery and Ontology-based services on the Grid Mario Cannataro

Attaching semantic annotations to service descriptions Luc Moreau

Semantic Matching of Grid Resource Description Frameworks John Brooke

Interoperability challenges in Grid for Industrial Applications Mike Surridge

Semantic Grid and Pervasive Computing David De Roure

E-Science Special Issue

IEEE Intelligent Issue Special Issue on
E-Science, Jan-Feb 2004

De Roure, Gil, Hendler

Challenges:

Realising the network effect

Moving beyond centralized stores

Automated assembly

Collaboration tools

GGF11 Semantic Grid Workshop

Engineering semantics: Costs and Benefits Simon Cox

Designing Ontologies and Distributed Resource Discovery Services for an Earthquake Simulation Grid Marlon Pierce

Exploring Williams-Beuren Syndrome Using myGrid Carole Goble

Distributed Data Management and Integration Framework: The Mobius Project Shannon Hastings

eBank UK - Linking Research Data, Scholarly Communication and Learning David De Roure

Using the Semantic Grid to Build Bridges between Museums and Indigenous Communities Ronald Schroeter

Collaborative Tools in the Semantic Grid David De Roure

The Integration of Peer-to-peer and the Grid to Support Scientific Collaboration

OWL-Based Resource Discovery for Inter-Cluster Resource Borrowing Hideki YOSHIDA

Semantic Annotation of Computational Components Peter Vanderbilt

Interoperability and Transformability through Semantic Annotation of a Job Description Language Jeffrey Hau

IST

OntoGrid Objectives

Strategic

Pioneer the use of Knowledge Technologies (KT) to enhance and extend architecture and design of Grid computing systems

Enable Deployment of Knowledge Technologies in Grid Architectures

Deploy prototypes in the context of real world Business Applications

Technical

Knowledge Services that are Grid Aware and Grid Compliant

Grid Services that are Knowledge Aware

Knowledge

Services

Grid

Services

Knowledge

Aware

Grid

Aware

X-Ray
e-Lab

Analysis

Properties

Properties
e-Lab

Simulation

Video

Diffractometer

Grid Middleware

Structures
Database

Combe Chem pilot project

www.smarttea.org

CombeChem Smart Tea

Smart Lab Snapshot

Grid

E-Scientists

Entire E-Science Cycle
Encompassing experimentation, analysis, publication, research, learning

Institutional Archive

Local
Web

Publisher Holdings

Digital Library

E-Scientists

Graduate Students

Undergraduate Students

Virtual Learning Environment

E-Experimentation

E-Scientists

Technical Reports

Reprints

Peer-Reviewed Journal & Conference Papers

Preprints & Metadata

Certified Experimental Results & Analyses

Data, Metadata & Ontologies

eBank

Ontology-based Resource Matching:
The Grid Meets the Semantic Web

Hongsuda Tangmunarunkit

Carl Kesselman

Center for Grid Technologies

Stefan Decker

Intelligence Division

Information Sciences Institute

University of Southern California

Condor Matchmaker

Exact syntactic matching

Examples: Condor Matchmaker, PBS

Use symmetric syntax (i.e., attribute-value pairs) to describe resources and requests

Type=“Machine”; Type=”Job”;

Constraints are specified by conjunctions and disjunctions of arithmetic/string comparisons and set membership Boolean operations

Memory > 1000

Example

Type=“Job”; Owner=“u2”;

Constraint=

other.Type==“Machine” && Memory>500

&& OpSys==“Solaris251”;

Rank=other.Memory

Type=“Machine”;

Name=“m2”;

Memory=1000;

OpSys=“Solaris251”;

Grp1={“u1”,”u2”}; Constraint=member(other.Owner, Grp1)

Type=“Machine”;

Name=“m1”;

Memory=1500;

OpSys=“Redhat7.3”;

Grp1={“u1”,”u2”};

Constraint=member(other.Owner, Grp1)

Machine=“m2”

Matchmaker

Resource Requesters

(users/programs)

Finding the Unix machine

A machine’s operating system is described as “SunOS” or “Linux.”

To query for a machine that is “Unix” compatible, a user either has to:

Explicitly incorporate the Unix compatibility concept into the request requirements by requesting a disjunction of all Unix-variant operating systems

e.g., (OpSys=“SunOS” OpSys=“Linux”)

The disjunctive requirements become unwieldy as more abstract concepts are developed.

Wait for all interesting resources to advertise their operating system as Unix as well as either Linux or SunOS

e.g., (OpSys= “SunOS,” “Unix”), and then express a match as set-membership of the desired Unix value in the OpSys value set

e.g., hasMember(OpSys, “Unix”).

The advertisements become more complex and all resources must be updated before a match can occur.

Solution 1

Type=“Job”; Owner=“u2”;

Constraint=

other.Type==“Machine” && Memory>500;

(OpSys==“Solaris251” || OpSys==“Redhat7.3”)

Rank=other.Memory

Type=“Machine”;

Name=“m2”;

Memory=1000;

OpSys=“Solaris251”;

Grp1={“u1”,”u2”};

Constraint=member(other.Owner, Grp1)

Type=“Machine”;

Name=“m1”;

Memory=1500;

OpSys=“Redhat7.3”;

Grp1={“u1”,”u2”}; Constraint=member(other.Owner, Grp1)

Machine=“m2”

Matchmaker

Resource Requesters

(users/programs)

Solution 2

Type=“Job”; Owner=“u2”;

Constraint=

other.Type==“Machine” && Memory>500;

member(“Unix”, other.OpSys)

Rank=other.Memory

Type=“Machine”;

Name=“m2”;

Memory=1000;

OpSys={“Solaris251”, “Unix”};

Grp1={“u1”,”u2”};

Constraint=member(other.Owner, Grp1)

Type=“Machine”;

Name=“m1”;

Memory=1500;

OpSys={“Redhat7.3”, “Unix”};

Grp1={“u1”,”u2”};

Constraint=member(other.Owner, Grp1)

Machine=“m2”

Matchmaker

Resource Requesters

(users/programs)

Pizza Ontology

Platform

Operating system

Unix

Windows

Cluster

Datastore

Insight from GRIP

The process of building the ontologies gave insight into the fundamental differences in conceptualisation and hence the barriers to interoperability

Globus (via the GLUE schema) attempts to model the abstract structure of the resources

“This is what we are, this is what we can do”

Unicore AJO abstracts the request for resources

“I want this work done in time for this event.”

Outline

The ambition

Enabling Technologies

Grid

Semantic Web

Semantic Grid

State of the Art

The Future

Grid services

Semantic Web Services

Semantics for the Grid

Grid services for Semantic Web

Plumbers

Developers

Web

Services

Grid

Semantic

Web

and Agents

Semantic

Grid

Engineers

Aesthetics

Theoreticians

Building Bridges

Goble

Semantic Grid Services

Service description, discovery

Service composition, workflow

Semantic matching vs e.g. Condor matchmaking

Quality of Service, availability

WSI+, WSRF, …

Rules language compatibility

Autonomy?

GGF13
Semantic Grid Research Group

David De Roure

Carole Goble

Geoffrey Fox

www.semanticgrid.org

Agenda

GGF IP policy, sign-up sheet, notetaker

Introduction to Semantic Grid (30mins)

Grid Resource Description ontology (5mins)

Semantic Web Services - Kashif Iqbal (20mins)

Aspects of agent-based computing and Grid computing

Agents and the Grid - Hiroki Suguri (15mins)

WS-Agreement – Wolfgang Ziegler (10mins)

Discussion to inform charter review (5mins)

Pervasive

Semantic

Grid

Pervasive applications

need the Grid,

e.g. Sensor Networks

Grid applications need

Pervasive Computing

e.g. Smart Laboratory

Grid and Pervasive share issues in large scale distributed systems.
e.g. service description, discovery, composition; autonomic computing.

These can be aided with semantics.

Fundamentally

about

Interoperability

and inference

Research agenda March 2005

Automated Virtual Organisation Formation and Management

Service Negotiation and Contracts

Security, Trust and Provenance

Metadata and Annotation

Content Processing and Curation

Knowledge Technologies

Design and Deploy

Interaction

Collaboration

Pervasive Computing

Discussion: single global system?

Knowledge representation, as this technology is often called, is currently in a state comparable to that of hypertext before the advent of the web: it is clearly a good idea, and some very nice demonstrations exist, but it has not yet changed the world. It contains the seeds of important applications, but to unleash its full power it must be linked into a single global system.

Tim Berners-Lee, inventor of the WWW, 2001

Community Events

Attend…

CCGrid 2005 Semantic infrastructure for Grid Applications workshop (May)

Ubiquitous Computing for e-Research workshop at NeSC (May)

Semantic Grid: The Convergence of Technologies, Dagstuhl (July)

GGF14 and/or GGF15

Semantic Grid conference in China, late 2005

WWW2006 in Edinburgh, May 2006

Thank you

Semantic Grid Contacts

David De Roure

University of Southampton, UK

dder@ecs.soton.ac.uk

Carole Goble

University of Manchester, UK

carole@cs.man.ac.uk

See www.semanticgrid.org

OGSI v1.0 introduces a new (Object-Oriented) infrastructure

OGSI: Standard Web Services Interfaces & Behaviours

Naming and bindings (basis for virtualization)

Every service instance has a unique name (Grid Service Handle) from which can discover supported bindings which are volatile (Grid Service Reference)

Two tiered naming scheme to cope with service migration and failover

Lifecycle (basis for fault resilient state management)

Service instances created by factories

Destroyed explicitly or via soft state

Information model (basis for monitoring & discovery)

Service data (attributes) associated with GS instances (SDEs)

Operations for querying (introspecting) and setting this info

Asynchronous notification of changes to service data

Service Groups (basis for registries & collective services)

Group membership rules & membership management

Base Fault type

To be fair all these questions were true until 2001 or so

Actually the originators are largely from Computer Science (

Biology will dominate the Grid

The largest science discipline:

The most scientists (Globally ~500,000-1,000,000)

The most research funding (Globally ~$50 Billion/year)

The most graduate students (>20,000 year)

Strong couplings to:

Medicine and human health

Agriculture and food supplies

Energy and ecology

Future industrial processes (bio-nano)

Consumer of other scientific technologies

Data Grids will dominate – computationally its probably as cheap to buy a cluster. It’s the interoperability for application integration that is cool

Actually it pre-dates this, and it is very much a W3C/TBL vision

Complexity could be a problem

Experiment API: Requests for information return subgraphs – not just triples – containing requested resource

e.g. getRecord() returns all processes and materials in the record

Semantic Grid Masterclass

Amsterdam, April 2005

David De Roure

2004-06-06T16:33:10