Service Composition in the Context of Grid

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					Service Composition in the Context of
               Grid
         Xiaofeng Du (Presenter), William Song, Malcolm Munro
                         University of Durham
        {xiaofeng.du, w.w.song, malcolm.munro}@durham.ac.uk




AHM2006, 21.09.2006
Outline


 • Problems and issues.
 • Web Service and Grid Service
 • Conceptual Service Composition
 • Semantic Service Description Aspects and
   Matchmaking
 • Summary and future work
Introduction

 • How to extract and represent semantics for the
   Web information and the Grid recourses has
   became a consensus issue in WWW and Grid
   research.
 • Grid provides an infrastructure for resource sharing
   and cooperation.
 • Interoperability and collaboration requires common
   understanding of information/data and resources.
 • The understanding requires canonical and well-
   formed semantic description.
Web Services and Grid Service

 • A Web service is a software system designed to support
   interoperable machine-to-machine interaction over a
   network that has an interface described in a machine-
   processable format.
 • A Grid service is a Web service that conforms to a set of
   conventions (interfaces and behaviours) that define how a
   client interacts with a Grid service. The conventions adhere
   to the OGSI specifications.
 • The context of the Grid is e-Science. Grid is the underlying
   computer infrastructure that provides these facilities to
   facilitate e-Science.
 • Web Service, realized in Grid services, uses a standardized
   XML messaging system and a stack of communication
   protocols to makes software available over the Web.
Type of Services (1)

 •       Services Types:

     •    To exchange information and documents;
     •    To interactively and cooperatively perform
          functions;
     •    To share available resources.
Type of Services (2)

 • The first type is the Web systems or software for
   information exchange.
 • The second one, we consider that web service is a
   chain of (sub-) services in order to accomplish a
   user given task, see the diagram.
 • Grid service is considered to
   be of the third type, which
   emphasizes cooperation and
   sharing of resources
   (including data) among
   entities.
Task, Service, Resource, and Grid

 • The Relationships among task, web service,
   resource, and grid infrastructure are
   illustrated in the diagram.
  • In the grid infrastructure, the resource
    sharing is task driven.
  • A resource consumer issues tasks to
    request resource sharing from the
    resource providers.
  • The web services can be considered
    as an interface between tasks and
    resources, which can consume
    suitable resources to achieve a certain
    task.
Service Composition (1)

 • Motivation
   • Build more powerful service using the basic existing
     services.
   • Better fulfil service requester’s requirement.
   • Enhance resource reuse and reduce the cost and time of
     new service development.
 • Service composition process
   •   Identifying sub tasks,
   •   Locating suitable sub-services,
   •   Formatting the sub-services into a service flow,
   •   Executing the service flow to achieve a task which is the
       goal of the composite service.
Service Composition (2)

 • Data type and Semantics need to be
   considered when composing services.
 • Some other contextual factors are also need
   to be considered, such as:
   • Time – multiple services invocation and
     synchronisation.
   • Data Consistency – data type, semantics, and
     units etc.
   • Location – service’s location.
   • etc.
Service Composition (3)

 • Inputs and outputs of a composite service
   are generated from some of its internal sub-
   services’ inputs and outputs.
 •   Use directed graph G(V, E) to represent all the
     services in the grid environment
                                                                                                v
                                                                    v                                         v
 •   Use Sub_G1(V 1, E1) to represent a composite                         vc
     service                                                                         Sub_G1               v
                 Sub _ G1 (V1 , E1 )  G                                                            C
                                                                                v1
 •   Use Sub_G2(V 2, E2) to represent a composite                                                        vc       v
                                                                          vc           v1      v1
     service together with its context.                                         v1
       Sub _ G2 (V2 , E2 )  G             Sub _ G1  Sub _ G2     v      vc                Sub_G2
 •   Then context of the composite service can be                                                             v
     represented as graph C(V c, Ec)                                       …           …             v
                                                                 The Grid (G)
          C (Vc , Ec )  Sub _ G2  Sub _ G1
                                                                 Note: the arcs in the graph G are uncertain
 •   Then the set of arcs Ec represent the inputs
                                                                 due to different composition requirements.
     and outputs of the composite service.
                                                                 Here just captures a possible situation.
                 Ec  I  O
More on Composition

 • Composition is important in services and
   resources reuse.
 • Manual work is unsatisfactory.
 • Automatic composition is preferred.
 • A comprehensive description model or
   framework is crucial to achieve automation.
Semantic Service Description Aspects

 •   IOPE: addresses the service’s input data type, output data type, pre-
     condition, and effects. It also addresses the semantic meaning of the
     inputs and outputs.
 •   Metadata: addresses the non-functional description of the service
     including identifier, natural language description, location, quality
     attributes, and category.
 •   Ontology: addresses the concept and domain of the service.
 •   Upper Compositionality: addresses which kind of services can be
     composed by using this service. This aspect indicates the
     relationships between this service and other services.
 •   Lower Compositionality: addresses what kind of services can be
     used to compose this service. This aspect also indicates the
     relationships between this service and other services.
 •   Resource: addresses what kind of resources the service will
     consume. The resources include renewable resources, such as
     bandwidth, memory allocation, and CPU usage, and consumable
     resources, such as time.
Contextual based Semantic Model

• Relationships among semantic description aspects.
 • The notation used in the
   diagram:
    • Si=Servicei                        S1-Si-S2          Service’

    • Service’ can be either the     is_a_component_of         has_parent
      parent or ancestor of
      Servicei                      Servicei-1
                                                    I, P
                                                            Servicei
                                                                            O, E
                                                                                         Servicei+1
    • S1, S2, S3, and S4 are any
      services.                       has_components                               consume

    • I, P is the Inputs and Pre-
                                          S3-…-S4          Metadata                   Resource
      condition, and O, E is the
      Outputs and Effects.
Matchmaking

• Semantic Distance Calculation. Based on
  the model introduced previously, a semantic
  distance calculation formula is proposed.



  • Here λ is the set of all the semantic characteristics
    functions.
  • The result value will be between 0 and 1.
  • This formula is not a new matchmaking method, but to
    examine how the model can benefit the service matching.
Matchmaking Example

• Service Requirements
    •    Number of inputs: 2,
    •    Input data type: double,
    •    Output data type: double,
    •    Description keyword: addition,
    •    A mathematics calculation service.
    •    Can be used to calculate the perimeter of a rectangle
  Matching Aspects                       Service1                        Service2                         Service3
                           double addition(double a, double b)   int addition(int a, int b)   double power(double a, double b)

  IOPE                                    1.0                               0.5                              1.0
  Ontology                                1.0                               1.0                              1.0
  Metadata                               0.667                             0.667                             0.0
  Upper Compositionality                  1.0                               1.0                               0
  Sum                                    3.667                             3.167                             2.0
  Distance                               0.92                              0.79                              0.5


  The Chosen service is Service1
Summary and Future Work

• Defining a contextual based semantic model to
  describe services is important in semantic web
  service, grid computing, and web services
  researches.
• It is particularly significant in semantic based
  automatic service searching, matchmaking, and
  composition.
• Consummate the contextual based semantic model
  to more sufficiently describe services.
• Develop an advanced quantitative analysis
  algorithm for semantic matching and to evaluate its
  performance.
References

1.   Foster, I. and Kesselman, C., The Grid: Blueprint for a New Computing
     Infrastructure, Publisher: Morgan Kaufmann Inc, 1999. ISBN: 1-555860-475-8
2.   Foster, I., Kesselman, C., and Tuecke, S., The Anatomy of the Grid: Enabling
     Scalable Virtual Organizations. International Journal of High Performance
     Computing Applications, 15 (3). 200-222. 2001
3.   Dey, A. K. and Abowd, G. D., Towards a Better Understanding of Context
     and Context Awareness. Presented at the CHI 2000 Workshop on the What,
     Who, Where, When, Why and How of Context-Awareness, April 1-6, 2000
4.   Fensel, D. and Bussler, C., The Web Service Modeling Framework WSMF.
     Electronic Commerce Research and Applications, Vol. 1, Issue 2, Elsevier
     Science B.V.
5.   Lara, R., Lausen, H., Arroyo, S., Bruijn, J., and Fensel, D., Semantic Web
     Services: Description Requirements and Current Technologies, In
     International Workshop on Electronic Commerce, Agents, and Semantic Web
     Services, September 2003.
6.   Medjahed, B., Bouguettaya, A., and Elmagarmid, A. K., Composing Web
     Services on the Semantic Web, the VLDB Journal 2003. Volume 12, pp. 333-
     351
7.   De Roure, D., Jennings, N. R., and Shadbolt, N. R. The Semantic Grid: Past,
     Present and Future. Procedings of the IEEE, 2005, Volume 93, Issue 3,
     pp. 669-681
Thank you for your attention and patience !
             Any Questions?

				
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