The Journal of Supercomputing

, Volume 49, Issue 1, pp 64–83 | Cite as

Advanced service trading for scientific computing over the grid

Article

Abstract

One of the great benefits of computational grids is to give access to a wide range of scientific software and computers with different architectures. It is then possible to use a huge variety of tools for solving the same problem and even to combine these tools in order to obtain the best solution.

Grid service trading (searching for the best combination of software and execution platform according to the user requirements) is thus a crucial issue. Trading relies on the description of available services and computers, on the current state of the grid, and on the user requirements. Given the large amount of services that may be deployed over the grid, this description cannot be reduced to a simple service name.

A sophisticated service specification approach similar to algebraic data type is presented in this paper. Services are described in terms of their algebraic and semantic properties. This is nothing else than proceeding to a description of algorithms and objects properties for a given application domain.

We then illustrate how this specification can be used to determine the service or the combination of services that best answer a user request. As a major benefit, users are not required to explicitly call grid-services, but instead manipulate high-level domain-specific expressions.

Our approach is fully generic and can be used in almost all application domains. We illustrate this approach and its possible limitations within the framework of dense linear algebra. More precisely, we focus on Level 3 BLAS (ACM Trans Math Softw 16:1–17, 1990; ibid 16:18–28, 1990) and LAPACK (Society for Industrial and Applied Mathematics, Philadelphia, 1999). Some examples in nonlinear optimization are also given to demonstrate how generic our approach is and report on experiments where both domains interact to show the multi-domain possibilities.

Keywords

Grid computing Semantic-based service trading Equational unification Linear algebra Multi-domain 

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References

  1. 1.
    Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J, Du Croz J, Hammarling S, Greenbaum A, McKenney A, Sorensen D (1999) LAPACK Users’ guide, 3rd edn. Society for Industrial and Applied Mathematics, Philadelphia Google Scholar
  2. 2.
    Astsatryan H, Daydé M, Hurault A, Pantel M, Caron E (2007) On defining a web interface for linear algebra tasks over computational grids. In: International conference on computer science and information technologies (CSIT’07), Yerevan, Arménie, 24/09/2007–28/09/2007 Google Scholar
  3. 3.
    Astsatryan H, Sahakyan V, Shoukouryan Y, Daydé M, Hurault A, Pantel M, Caron E (2008) Using ontology for resources matchmaking in grid middleware. In: VECPAR conference, 2008 Google Scholar
  4. 4.
    Antoniou G, van Harmelen F (2003) Web ontology language: Owl. In: Staab S, Studer R (eds) Handbook on ontologies in information systems. Springer, Berlin Google Scholar
  5. 5.
    Castagna G, Ghelli G, Longo G (1992) A calculus for overloaded functions with subtyping. In Proceedings of the ACM conference on Lisp and functional programming, vol 5, 1992, pp 182–192 Google Scholar
  6. 6.
    Dongarra J, Du Croz J, Duff I, Hammarling S (1990) Algorithm 679. A set of level 3 basic linear algebra subprograms. ACM Trans Math Soft 16:1–17 MATHCrossRefGoogle Scholar
  7. 7.
    Dongarra J, Du Croz J, Duff I, Hammarling S (1990) Algorithm 679. A set of level 3 basic linear algebra subprograms: model implementation and test programs. ACM Trans Math Soft 16:18–28 MATHCrossRefGoogle Scholar
  8. 8.
    Daydé M, Desprez F, Hurault A, Pantel M (2005) On deploying scientific software within the GRID-TLSE project. Comput Lett 1(3):85–92 CrossRefGoogle Scholar
  9. 9.
    Erl T (2005) Service-oriented architecture: concepts, technology, and design. Prentice Hall PTR, Upper Saddle River Google Scholar
  10. 10.
    Goguen J, Meseguer J (1992) Order-sorted algebra i: equational deduction for multiple inheritance, overloading, exceptions and partial operations. Theor Comput Sci 105(2):217–273 MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Gallier J, Snyder W (1989) Complete sets of transformations for general e-unification. Theor Comput Sci 67(2–3):203–260 MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Hurault A, Aida K (2007) Using ontology for resources matchmaking in grid middleware. Rapport de recherche IRIT/RT–2007-8–FR, IRIT, Toulouse Google Scholar
  13. 13.
    Hurault A (2006) Courtage sémantique de services de calcul. PhD thesis, INPT, Toulouse Google Scholar
  14. 14.
    McGuinness D, van Harmelen F (2004) OWL web ontology language overview. W3C Recommendation Google Scholar
  15. 15.
    Pantel M (2004) Test of large systems of equations on the grid: meta-data for matrices, computers, and solvers. In PMAA’04, 2004 Google Scholar
  16. 16.
    Soloviev S, Di Cosmo R (eds) (2005) Isomorphism of types. Mathematical structures in computer science, vol 15. Cambridge University Press, Cambridge, http://www.cambridge.org Google Scholar
  17. 17.
    Stickel M, Waldinger R, Lowry M, Pressburger T, Underwood I (1994) Deductive composition of astronomical software from subroutine libraries. In: Conference on automated deduction, 1994, pp 341–355 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.IRIT—ENSEEIHTToulouse cedex 7France

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