Holding slide prior to starting show

(Some) Key Issues in Grid Computing

David WalkerSchool of Computer Science

Cardiff University

http://users.cs.cf.ac.uk/David.W.Walker

Main Thesis of Talk

• At a surface level many aspects of Grid Computing appear to be straightforward, and reduce to simple programming tasks and the use of existing tools.

• This talk aims to show that for domain scientists to effectively use the Grid many challenging CS issues need to be addressed.

What is the Grid?

• The Grid is an emerging communication and computational infrastructure for the transparent sharing of distributed computing resources.

• Resources include computers, data, instruments, sensors, visualisation platforms, and sometimes even people.

The Grid is more than just a faster Internet

Cambridge

Newcastle

Edinburgh

Oxford

Glasgow

Manchester

Cardiff

Southampton

London

Belfast

DL

RAL Hinxton

UK Grid Network National Centre in

Edinburgh/Glasgow 8 regional centres Grid support centre

Why is the Grid Important?

• It will give access to more computational power.

• It will make more computing and data resources more readily available.

• It will enable collaborative working and resource sharing through virtual organisations and communities.

• It will create new economic resources, products, and services.

Benefits from the Grid

• Empowering individuals and organisations.

• Enhancing national economic competitiveness, security, and quality of life.

• An engine for social transformation.

Areas of Impact

• e-Science: large-scale collaborative multi-disciplinary science in areas such as high energy physics, bio-informatics, astrophysics, chemistry, etc.

• e-Business: streamline, distribute, and enhance business processes.

• e-Government: transform relations between government and citizens, businesses, and other arms of government. – Better delivery of government services to citizens– Improved interactions with business and industry– Citizen empowerment through access to

information.

Grid Model - the Information Utility

MIDDLEWARE

Experiment

Experiment

Computing

Computing

Computing

Storage

Storage

Storage

Analysis

AnalysisScientist

CLRC Daresbury

Scientist

Scientist

A Typical Scientific Process

Key Elements of the Grid

• The specification of problems – how do you program the Grid?

• The dynamic discovery of Grid resources.• Provenance support for Grid applications.• The interoperability and federation of different

Grid middleware stacks.• Grid access to legacy applications.• Support for remote collaboration over the

Grid.

A Simple Example

• A simple use of the Grid involves the use of a PSE or portal to do a set of pre-determined tasks.

• This corresponds to the “utility computing” mode of use.

• No support for building new applications or services.

• No support for dynamic discovery of resources.

• No support for collaboration.

Programming the Grid

• Problem specification could involve– Use of high-level domain-specific

programming/scripting language.– Representing coordinated tasks with a

workflow graph assembled in a visual programming environment.

– Use of recommender systems to assist users in formulating and solving problems.

Workflow

• Commonly used to represent applications composed of interacting services.

• Services may be hierarchical – composed of other services.

• Easy to represent graphically, but not scalable with number of services or number of inputs/outputs.

Workflow Composition

Would like to support the domain scientist in designing workflows to solve problems.

Problems in Workflow Composition

• How do you know that the input port of one service is compatible with the output port of another service?

• Given that the services may have been created by different people/organisations?

• Type signatures must match, but semantics must also match.

Annotating Services

• To support “plug-and-play” between services in a workflow requires the use of ontologies.

• Need to give semantic content (meaning) to service inputs and outputs.

• This allows composition hints in the form of “semantic suggestions”. For example, for a given service port we could find all services that could be connected to it.

Types of Workflow Composition

Manual

User generates workflowgraphically or through

text editor.

TrianaBPWS4J

Self-Serve

Semi-automated

“Semantic suggestions”User still has to select theservice required from a

shortlist.

Cardoso & ShethGEODISEmyGRID

Sirin , Hendler et al.,

Automated

The entire composition is automated using AI

technologies.

SHOP2

Pegasus – ISIMcIllraith

IRS-II

Workflow Composition in Semantic Grids

• Semantic Web technologies enable automation at several levels – automated resource discovery, selection, management, service composition, execution.

• Promises automated seamless interoperation of autonomous, heterogeneous distributed applications.

• Our focus is on the use of Semantic Web technologies to automate service composition in Grid environments.

• See S Majithia, DW Walker, and WA Gray “Automatic Composition of Web Services,” in Proceedings of the UK e-Science Programme All-Hands Meeting 2004. Available online at http://www.allhands.org.uk/proceedings/papers/148.pdf

• Main developer is Shalil Majithia.

Framework - OverviewWFMS – Workflow Manager Service

AWFC – Abstract Workflow Composition ServiceCWFC – Concrete Workflow Composition Service

RS – Reasoning Service

MMS – Matchmaking Service

AWFR – Abstract Workflow Repository

CWFR – Concrete Workflow Repository

RB - Rulebase

AWFC CWFC

RS MMS

RB AWFR CWFR

WFMS

High level objective

Framework - Interactions

Client WFMS CWFCAWFC WFEE

1

2

3

4

5

6

7

8

1. High Level Request 5. Composed Concrete WF2. Request for Abstract WF 6. Request for Execution3. Composed Abstract WF 7. Results or Request for Alternatives4. Request for Concrete WF 8. Final Results

Abstract Workflow Composer

• An abstract workflow specifies a workflow without referring to a specific service implementation .

• The Abstract Composer tries to generate an abstract workflow by using:– AWF Repository: stores semantically annotated

descriptions of services and workflows. Use ontology to match services.

– Rulebase: a rulebase specifies the “recipe” to achieve an objective

– Chaining services: try and chain services by matching service outputs and inputs.

Concrete Workflow Composer

• A concrete workflow specifies an executable workflow by referring to specific service implementations.

• The Concrete Composer tries to generate an executable workflow by using:– Matchmaking: match abstract workflow with

service implementations available at that time.– Chaining services: try and chain services by

matching service outputs and inputs.

Other Components

• Matchmaker service (based on that of Paolucci et al.) adapted for dynamic substitution.

• Chaining service: backward chaining service based on domain ontologies.

• Repositories: store semantically annotated abstract and concrete workflows.

Implementation• All components

implemented as Web services using Axis server.

• Services and workflows described using OWL-S.

• DQL/JTP server used for subsumption reasoning

• Rulebase implemented in RuleML

• Plug-in module enables generation of concrete workflows in BPEL4WS.

<profileHierarchy:SignalProcessing rdf:ID="FFT"><profile:input>

<profile:ParameterDescription rdf:ID="FFTInput"><profile:restrictedTo

rdf:resource="Concepts.owl#VectorType"/></profile:ParameterDescription>

</profile:input><profile:output><profile:ParameterDescription rdf:ID="FFTOutput">

<profile:restrictedTordf:resource="Concepts.owl#ComplexSpectrum"/>

</profile:ParameterDescription></profile:output>

</profileHierarchy:SignalProcessing>

<profileHierarchy:SignalProcessing rdf:ID="FFT"><profile:input>

<profile:ParameterDescription rdf:ID="FFTInput"><profile:restrictedTo

rdf:resource="Concepts.owl#VectorType"/></profile:ParameterDescription>

</profile:input><profile:output><profile:ParameterDescription rdf:ID="FFTOutput">

<profile:restrictedTordf:resource="Concepts.owl#ComplexSpectrum"/>

</profile:ParameterDescription></profile:output>

</profileHierarchy:SignalProcessing>

Snippet of OWL-S Profile for FFT

Family Tree Example

• Families trees have 3 basic relationships– Spouse_of– Child_of– Parent_of

• Other relationships (aunt, grandparent, cousin, etc) can expressed in terms of these relationships through an ontology.

Cousins Example

• Suppose we want to create a workflow to find the cousins of a given person, X.

• Query is submitted to WFMS which checks the AWF repository (i.e., checks annotated name of workflows)

• If no match then check rule base

Rulebase

Grandparents(X)=Parents[Parents[X]]

Cousins(X)=exclude[Grandchildren[Grandparents(X), Children[Parents[X]]]]

Note: There is no rule for Grandchildren[X]. The Chaining Service would deduce how to do this from the ontology.

Abstract Workflow From Rulebase

X

Grandparents Grandchildren

Parents Children

ExcludeCousins

Atomic service

Composite service

WF after Recursive Application of Rulebase

X

Parents Parents

Parents Children ExcludeCousins

Grandchildren

WF after Application of Chaining Service

X

Parents Children ExcludeCousins

Parents Parents Children

Children

Note opportunity for optimization and parallelism.

Dynamic Resource Discovery and Scheduling

• Assume that semantically annotated services can be found through a registry or repository service.

• Scheduling of workflow nodes on distributed resources.– Early binding model: bind to specific service/platform at

composition time (“validation”).– Intermediate binding model: bind at “compile” time (when

converting from XML to executable form).– Late binding model: bind dynamically at runtime.

• Later binding allows the use of more up-to-date information to make scheduling decisions.

• In our framework binding is done by the Matchmaker Service, and can follow any of the above binding models.

Provenance Support in Service-Oriented Grids

• A workflow may produce many intermediate and final data products that may need to be later reviewed and analysed.

• A person, project, or organisation may need to archive many such workflows and their results.

• Want to store the provenance of data products: how they were produced and why.

• Main developer is Shrija Rajbhandari.

Provenance

• Provenance can be regarded as historical metadata that provides an explanation of how a particular data product has been generated.

• Uniquely defines the derived data.• Identifies what data is passed between

services.• Provides a traceable path to the origin of

the data.

Provenance Importance and Problem

• No known standards to support archiving provenance in service-oriented Grid environment.

• Requires recording the provenance:– The transformation of data occurred during

the invocation of services in a workflow. – Complex service executed via a workflow

Engine.

Original Motivation

• Would like to be able to view an electronic publication, and click on tables and figures of results to:– See how they were generated: requires

provenance browser.– Re-run the workflows that generated the results to

verify them, or to perform “what-if” study by changing the workflow inputs.

– See the results of any re-run workflows in the same format as the original data (table of graph).

Provenance Model

Provenance mySql

Database

Provenance Server

PCS

PQS

Workflow Engine

[BPWS4J]

JENA

RDF Schema

INTERFACE

PCS = Provenance Collection ServicePQS = Provenance Query ServiceJena is a Java framework for building Semantic Web applications. http://jena.sourceforge.net/

Prototype Provenance System

• Provenance Schema– Resource Description Framework (RDF).– Provenance of workflow execution.

• Provenance Collection Service (PCS)– Provenance is represented in RDF statements.– Database storage.

• Provenance Query Service (PQS)– Client interface to browse provenance.– Allows re-execution of retrieve provenance for

“what- if” style of analysis.

7) PQS Client passes query to the database server which returns the provenance data using Jena tools to access RDF data.

Prototype Dataflow

8) PQS allows re-execution of the workflow from the provenance data retrieved. Also allows parameter changes during re-execution of such workflow.

PCS

PQS Client Interface

PCS Client Interface

3) BPWS4J invokes the partner services

Web Services

1) User Client Interface sends the workflow invocation parameters to PCS.

4) BPWS4J sends message about invoked services, and the input and output parameters to PCS

2) PCS sends the invocation initiation of a workflow to BPWS4J. BPWS4J

Engine

6) PCS stores the RDF graph in the database server using Jena toolsProvenance

Database

5) PCS Creates RDF representation of the collected provenance data of the workflow execution

Provenance RDF schema

Uses

Services Composition and Invocation

• Compose Web services using BPEL4WS• Execute with BPEL4WS compliant

engine: IBM’s BPWS4J• Dynamically invoke Web services using

Web Service Invocation Framework (WSIF).

Provenance RecordingExample: Adding two numbers and multiplying the result with

a third number

Provenance Recording (cont..)

Provenance Recording (cont..)

Provenance Query

Re-execution for “what-if” analysis

Other Grid Projects

• Quality of Service: http://www.cs.cf.ac.uk/user/Rashid/

• Resource-Aware Visualization Environment (RAVE): http://www.wesc.ac.uk/projects/rave/

• Grid-Enabled Computational Electromagnetics (GECEM): http://www.wesc.ac.uk/projects/gecem/

• Workflow Optimization Services for e-Science (WOSE): http://www.wesc.ac.uk/projects/wose/

Summary

• Semantic Web technologies play a key role in enabling;– “plug-and-play” in the composition of service to

create workflows.– dynamic discovery of resources.– Support for provenance.

• The above, together with collaborative visualisation, are important in convincing scientists (and others) to use the Grid.