march 23, 2006m.i.t., anna univ, chennai 1 development of front end tools for semantic grid services...

March 23, 2006 M.I.T., Anna Univ, Chennai 1

Development of Front End tools for Semantic Grid

Services

Dr.S.Thamarai Selvi,

Professor & Head,

Dept of Information Technology,

Madras Institute of Technology, Anna University,

Chennai.

Review Meeting – March 24, 06


Objective

To develop a Front End Tools for Semantic Grid Services that enables

the Service Requester to search a particular Grid Service/Resource Semantically.

the Service Provider to describe a Grid Service/Resource Semantically.


Activities

Study of current version of Globus Toolkit and study of Semantic Grid Services

Understand the grid architecture and study of globus toolkit. Study of languages needed to implement semantic grid services.

A prototype model for semantic grid services

Extending the UDDI registry to include semantic advertisements using TModels. Design and Development of algorithms for intelligent discovery of grid services.

Design and Development of Grid Resource Portal

Functional testing and optimization of implementation


Road Map

Understanding various Components of Globus Toolkit 4.0

Understanding Semantic Web Services.

Understanding the technology used to develop Semantic

Web Services.

Understanding Semantic Grid Services.

Developing a typical prototype for Semantic Grid

Services.


Layered Architecture of semantic grid service

Concepts and Tools to build semantic web applications

that includes:-

*Protégé, an OWL editor.

*Algernon, an inference engine

*OWLS for service descriptions

The concept of Matchmaking of services using OWLS

An application has been built that retrieves

information from resource ontology using algernon.

Summary of last review meet – Sep 02, 2005Report


Creation of resource ontology may be automated

* We have developed a java module using protégé-OWL APIs to

develop and manage ontology. We can use this module for managing

resource ontology

* We are also developing a tool to convert WSDL into OWLS with

which we can create service ontology without manual intervention.

Aggregation of resource information may be automated ( may use

RFC 2016 and RFC 2608)

* We made thorough literature survey of GIIS of Globus toolkit

and we identified wsrf-query and grid-info-search toolS in Globus toolkit

with which we can aggregate the grid resource information

Summary of last review meet – Sep 02, 2005Observations and Suggestions


Status as on March 20, 2006

Implementation Issues have been identified

Several Approaches for implementing Knowledge

layer have been identified.

Implementation of Semantic Grid Architecture

using

proposed approaches.

Proposal of Versatile Knowledge layer.


Focus Today

Semantic Grid Architecture

Difficulties with Conventional Mechanisms

Proposed Approaches for Knowledge Layer

Semantic Grid Architecture using Protégé Enabled Globus

toolkit(PEG)

Semantic Grid Architecture using Resource Ontology Template

Further Scope

Conclusion


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

Semantic

Grid


Distributed Resources

Computation Services Layer

Data Services Layer

Information Services Layer

Knowledge Layer

Semantic Grid Architecture

Resources Includes Supercomputers, clustersWorkstations etc.,

This layer Manages allocation of computational resources, Job Execution,Secure Access to grid resources

This layer deals with the way resources are represented, stores, shared and Maintained

This layer act as an infrastructure to support the management and application of scientific knowledge to achieve particular types of goal and objective shared and Maintained


Related Tools

Ontology

You need an Editor to Create Ontology

Inference Engine

To retrieve Knowledge from Ontology


Ontology

Ontologies are used to capture knowledge about some domain of

interest.

Ontology describes the concepts in the domain and also the

relationships that hold between those concepts

Complex concepts can therefore be built up in definitions out of simpler

concepts.

Ontology Web Language (OWL) is widely used to create

Ontology

Ex : Protégé, an OWL editor


Limitation of OWL

Though OWL has a well-defined semantics, but

it is not sufficiently expressive to characterize and describe

services

So, OWL-S, OWL for Service


OWL-S

OWL-S is an OWL-based web service ontology, which supplies a core set of markup language constructs for describing the properties and capabilities of web services in unambiguous, computer interpretable form


Limitations of OWLS

Though OWLS has WSDL2OWLS, but it cannot convert Grid WSDL to OWLS.

It cannot recognize WSRF specific WSDL elements.

Hence we need to compromise while using the tool

WSDL2OWLS


Currently there is no tool available to create Grid Service Ontology automatically from its WSDL file

Difficulties

• We need to create Service Ontology using Protégé Editor

• Need to identify a tool to convert Grid WSDL into OWLS descriptions.

• Need to automate semantic descriptions of resource

Solution

Challenges in OWLS


Proposed Approach

Semantic Description and Discovery of Grid Services We propose and implement Semantic Grid Architecture by integrating protégé editor with

Globus Toolkit and implements Parameter Matchmaking Algorithm for semantic discovery of services.

Semantic Description and Discovery of Grid Resources We propose a five layered semantic grid architecture using Gridbus broker that addresses the

need of semantic component in the grid environment to discover and describe the grid resource semantically

Also It is decided to devise a knowledge layer for semantic description of resources and its

retrieval, for semantic description of services and matchmaking of advertised grid services against the requested ones.


Semantic Description and Discovery of Grid Services


Protégé Enabled Globus Toolkit

Globus Toolkit (GT) lacks a component to describe concepts semantically.

In PEG, protégé has been integrated into Globus Toolkit 4.0

It addresses the demands of a single toolkit to build grid infrastructure as well as for semantic description and representation of services and resources.

Globus user now can develop ontology for their services.


Architecture of Semantic Grid Service using PEG

Semantic Discovery portlet

Grid Information Portlet

Application Portlet

Application Layer

Knowledge Layer

Computational GridServices (High level

Grid Services)

Grid MiddlewareServices

Fabric Layer

TokenizerSemantic

ComponentService Ontology

Information Data Management File/Data

MDS GRAM GridFTP

Protégé_3.1

GT4 MiddlewareAuthentication Authorization

GSI

ResourcesR1R2 R3

R4



Application Portlet



Application Portlet

Application Layer

Knowledge Layer

Computational GridServices (High level

Grid Services)

Grid MiddlewareServices

Fabric Layer

TokenizerSemantic


TokenizerSemantic


Information Data Management File/Data

MDS GRAM GridFTP

Protégé_3.1Information Data Management File/Data

MDS GRAM GridFTP

Protégé_3.1

GT4 MiddlewareAuthentication Authorization

GSI

ResourcesR1R2 R3

R4ResourcesR1R2 R3

R4


Service Matchmaking

Refers to capability matching which means to compare requested

service description with the advertised service description to

determine how similar they are.

Matchmaking algorithm uses Inputs and Outputs for matching

and does not consider Preconditions and Effects as they are not

sufficiently standardized to be considered for matchmaking

On an optional basis, other properties can also be taken into

account assuming they have been described using any specific

ontology language such as OWL.

In our Parameter Matchmaking Algorithm, We propose to use

Inputs,

Outputs and Functionality for matchmaking of services.


Parameter Matchmaking Algorithm

We introduce Parameter Matchmaking Algorithm that

computes degree of matching of service advertisement (A)

and request (R)

The algorithm compares IOF of A and that of R, computes

various degree of matches namely:-

Exact: A and R exactly matches A(IOF) ≡ R(IOF)

Plug-in: A offers more functionalities than R A(IOF) ≥ R(IOF)

Subsume: R requests more functionalities than advertised A

A(IOF) ≤ R(IOF)

Intersection: Not all functionalities matches A(IOF) ∩ R(IOF)

Disjoint: A and R does not match A(IOF) ≠ R (IOF)


Processes Involved

Ranked Degree of Match

InputMatching

OutputMatching

FunctionalityMatching

A(I)R(I) R(O) A(O) R(F) A(F)

AggregateModule

Ir Or Fr


Intermediate Ranks afterComparing I, O, F individually

Computes Final ranked Degree of match


Functional Model of the Knowledge Layer


Service Provider

A GridService that implements four functionality namely Addition, Subtraction,

Multiplication and Division.

Service Ontology has been created using Protégé editor to describe the service.

Implementation

A sequence diagram of service provider


Service Requester

The requester submits Query.

The semantic component extracts F from the query (RF) and also from service ontology (AF).

It compares RIOF with AIOF and computes ranked degree of match

{exact, plugin, subsume, intersection, disjoint}

Sequence diagram of service requester


Sl.No Capability Requested Degree of Match Possibility of service invocation

1 Addition and Subtraction Plug in True

2 Addition, Subtraction, Multiplication and

Division

Exact True

3 Addition, Subtraction and Reversal of

string

Intersection True

4 Squaring and Temperature service Disjoint False

5 Addition, Subtraction, Multiplication and Division, Temperature Service

subsume True

6 Multiply, add, divide Plug in True

7 Square service Disjoint False

8 Addition and factorial Intersection True

9 Add, sub, divide and Multiply Exact True

Experimental Results


Snapshots


Algorithm: Parameter Matchmaking Algorithm Input: Advertised_Ontology A, Requester_query R Output: Degree_of_Match M Rank: input_rank,output_rank,functionality_rank parse A into A(I1,I2,..Im),A(O1,O2,..On) and A(F1,F2,..Op) parse R into R(I1,I2,..Ir),R(O1,O2,..Os) and R(F1,F2,..Ot) c1=0, c2=0,c3=0 for each parsed A( I1,I2,..Im), A(O1,O2,..Om), A( F1,F2,.Fm) do if A(Ii)== R(Ij) then c1++; if A(Oi)== R(Oj )then c2++; if A(Fi)== R(Fj) then c3++; end if end for

input_rank=compute_intermediaterank(m,c1,r) output_rank=compute_intermediaterank(n,c2,s) functionality_rank=compute_intermediaterank(p,c3,t) M=leastof(input_rank, output_rank, functionality_rank) Rank compute_intermediaterank(i,c,j) { if(i==c==j) then R=1; if(i>c=j), then R = 0.75; if(i=c<j), then R =0.50; if(i>c<j), then R = 0.25; if(i!=c!=j), then R = 0; }



The Scenario of plug in match


The Scenario of Exact match


The Scenario of Intersection match


The Scenario of Disjoint match


Semantic Description and Discovery of Grid Resources


Objective

To propose a five layered semantic grid architecture with

knowledge layer at the top of gridbus broker

Knowledge layer – Semantic grid resource description using

adaptive ontology template and Knowledge discovery using

Algernon inference engine.


Motivation

Conventional mechanisms

UDDI

MDS

They offer searching mechanism based on keywords.

The node providers need to agree upon attribute names and values.

In grid like environment, where resources come and go there is

always a demand for framework to support semantic description and

discovery of services and resources.


A Five Layered Architecture of Semantic Grid Services


Knowledge Layer Comprises two modules – Semantic Description and Discovery

Semantic Description

Domain Knowledge of grid is represented in ontology template

MDS is used to ‘plug’ grid resource information

Protégé-OWL APIs are used to build knowledge base of the grid using ontology

template

Semantic Discovery

Algernon inference is used to retrieve resource information

Job Descriptor

Creates Application Description File and Resource Description File to run the broker


Ontology Template

Definition – 1

Any resource can be modeled as an instance of a specific class provided that the

resource can be described using the properties defined in that class.

Definition – 2

An ontology template is the domain specific ontology that provides hierarchy of

classes with properties to define characteristics.

Protégé-OWL APIs are used to describe grid resources in the ontology template.


Resource Ontology Template


Grid Resource Knowledge base


QueryGenerator

User

ResourceDescription

GridBus Broker

Job execution

ResourceDiscovery

Resource

App. Des FileRes. Des File

QueryingOWL file

AlgernonQuery

MDS

Request

User

Submit Job

Semantic Repository

Results

To user …

Job Descriptor

Resource Information


Semantic Component



GIIS service runs on globus machine will retrieve resource information of the local host and stores it in LDAP server from where we can query the information.Protégé-OWL provides versatile libraries with which one can manage ontology and knowledge base. With those APIs insertion and removal of resources are possible

OWLNamedClass computerC=owlmodel.getOWLNamedClass("WorkStation");

OWLDatatypeProperty hasIP = owlModel.getOWLDatatypeProperty("hasIP");

cpuI.addPropertyValue(owlModel.getOWLObjectProperty("hasCPUVendor"),cVendorI);

computerI.addPropertyValue(owlModel.getOWLObjectProperty("hasCPU"),cpuI);


Semantic Discovery

We use Algernon Inference Engine to retrieve information

semantically.

This module accepts user query in the form of A:opB and converts it

into Algernon query to interact with the knowledge base.

Once suitable resource is discovered, user’s job will be submitted to

gridbus broker for execution.

This Knowledge Layer is implemented in Gridbus Broker, it can support

most of the popular middlewares including Globus, Alchemi etc.,


Snapshots


Protégé Ontology Editor


Further Scope

Semantic Grid Architecture using PEG Currently, service descriptions are done manually. We are in the verge of

developing a tool to convert WSDL of WSRF services into OWS

descriptions thereby overcoming the limitation of human intervention for

creating service ontology.

Wide literature survey of domain ontology is required.

Ontology clustering can be implemented to improve the performance of

matchmaking.

Storing OWLS descriptions into UDDI registry is to be resolved for better

management of semantic descriptions


Semantic Grid Architecture using Resource Ontology Template

The discovery module relies on the power of Inference engine

used to retrieve information semantically from the Knowledge

base. Since Algernon is an rule based inference engine, we need

to implement rules to improve the efficiency of searching

Mechanism

Workflow Engine

Integrating Workflow component with the knowledge

layer.


Extended Knowledge Layer

With these observations and scope, we present a versatile

knowledge layer that performs,

Semantic Description of resource using ontology template

Semantic description of services using GridWSDL2OWLS

Managing OWLS descriptions in UDDI registry

Clustering the OWLS descriptions using appropriate clustering

mechanism

Implementation of QoS based Matchmaking Algorithm with the help

of domain ontology.

Implementation of Rule based semantic search engine


Extended Knowledge Layer


Conclusion

The semantic grid architecture using PEG enables the service providers to describe

their grid services semantically. Whereas, the architecture using Gridbus broker,

provide semantic descriptions of grid resources using grid resource ontology

template.

We also identify the necessity of GridWSDL2OWL-S tool and is being developed in

our Grid Computing Laboratory. We made a wide literature survey of ontology

clustering with which the performance of ontology matchmaking can be improved.

With these observations, we propose a versatile knowledge layer which can be

implemented in the grid architecture that performs semantic descriptions of grid

resources, WSDL description of WSRF services into OWL-S descriptions, Discovery

of Suitable Grid resources, Ontology clustering and QoS based Matchmaking

algorithm. With these sophisticated features implemented in architecture will result in

versatile front end for implementing semantic grid services.


Appendices


Ontology Framework

WebServices

WebServices

WSRFServices

OWL

OWL-S

OWL

OWL

RBAC

uses

descr

descr

descrProduct/ProcessModel

BusinessProcessOntology

OrganisationalOntology

ServiceOntology

ResourceOntology

User/RoleAuthorisations

Services

SystemResources represents

represents

representsAuth.

Service

Ontology-BasedVirtual User Desktop

DistributedDistributedrun-time run-time

environmentenvironment

ref

ref

uses

uses

WSDL

grid

run on

?

Courtesy: Global Grid Forum 16Athens, Greece, February 13-16, 2006


Life without BrokerCourtesy: University of Melbourne,Gridbus Broker Presentation


Life with Broker

Scheduling?

Courtesy: University of Melbourne,Gridbus Broker Presentation


Questions


Thank YouThank You

march 23, 2006m.i.t., anna univ, chennai 1 development of front end tools for semantic grid services...

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