ICENI: optimisation of component applications within a Grid environment

  • Authors:

  • Nathalie Furmento;Anthony Mayer;Stephen McGough;Steven Newhouse;Tony Field;John Darlington

  • Affiliations:

  • London e-Science Centre, Imperial College of Science, Technology and Medicine, 180 Queen's Gate, London SW7 2BZ, UK;London e-Science Centre, Imperial College of Science, Technology and Medicine, 180 Queen's Gate, London SW7 2BZ, UK;London e-Science Centre, Imperial College of Science, Technology and Medicine, 180 Queen's Gate, London SW7 2BZ, UK;London e-Science Centre, Imperial College of Science, Technology and Medicine, 180 Queen's Gate, London SW7 2BZ, UK;London e-Science Centre, Imperial College of Science, Technology and Medicine, 180 Queen's Gate, London SW7 2BZ, UK;London e-Science Centre, Imperial College of Science, Technology and Medicine, 180 Queen's Gate, London SW7 2BZ, UK

  • Venue:
  • Parallel Computing - Special issue: Advanced environments for parallel and distributed computing
  • Year:
  • 2002

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Abstract

Effective exploitation of Computational Grids can only be achieved when applications are fully integrated with the Grid middleware and the underlying computational resources. Fundamental to this exploitation is information. Information about the structure and behaviour of the application, the capability of the computational and networking resources, and the availability and access to these resources by an individual, a group or an organisation.In this paper we describe Imperial College e-Science Networked Infrastructure (ICENI), a Grid middleware framework developed within the London e-Science Centre. ICENI is a platform-independent framework that uses open and extensible XML derived protocols, within a framework built using Java and Jini, to explore effective application execution upon distributed federated resources. We match a high-level application specification, defined as a network of components, to an optimal combination of the currently available component implementations within our Grid environment, by using composite performance models. We demonstrate the effectiveness of this architecture through the high-level specification and solution of a set of linear equations by automatic and selection of optimal resources and implementations.