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Resources must be made available to design, build and maintain Grids that are of high capacity (rich in resources), of high capability (rich in options), persistent (promoting stable infrastructure and a knowledgeable workforce), evolutionary (able to adapt to new technologies and uses), usable (accessible, robust and easy-to-use), scalable (growth must be a part of the design), and able to support/promote new applications Today, many groups are looking beyond the challenges of developing today s Grids to the research and development challenges of the future In this section, we describe some key areas that will provide the building blocks for the Grids of tomorrow 161 Adaptative and autonomic computing The Grid infrastructure and paradigm is often compared with the Electric Power Grid [127].

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On the surface, the analogy holds up the Grid provides a way to seamlessly virtualize resources so that they can provide access to effectively in nite computing cycles and data storage for the user who plugs in to the Grid The infrastructure managing which machines, networks, storage and other resources are used is largely hidden from the user in the same way as individuals generally do not know which power company, transformer, generator and so on are being used when they plug their electric appliance into a socket The analogy falls short when it comes to performance Power is either there or not there To the rst order, the location of the plug should not make electrical devices plugged into it run better However, on the Grid, the choice of the machine, the network and other component impacts greatly the performance of the program.

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Smart laboratories: We believe that for e-Science to be successful and for the Grid to be effectively exploited much more attention needs to be focused on how laboratories need to be instrumented and augmented For example, infrastructure that allows a range of equipment to advertise its presence, be linked together, annotate and mark-up content it is receiving or producing Service-oriented architectures: Research the provision and implementation of Grid facilities in terms of service-oriented architectures Also, research into service description languages as a way of describing and integrating the Grid s problem-solving elements Agent-based approaches: Research the use of agent-based architectures and interaction languages to enable e-Science marketplaces to be developed, enacted and maintained Trust and provenance: Further research is needed to understand the processes, methods and techniques for establishing computational trust and determining the provenance and quality of content in Grid systems.

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This variation in performance can be leveraged by systems that allow programs to adapt to the dynamic performance that can be delivered by Grid resources Adaptive computing is an important area of Grid middleware that will require considerable research over the next decade Early work in the academic community (eg the AppLeS project on adaptive scheduling [23], the GrADS project on adaptive program development and execution environments [69], Adaptive Middleware projects [128], the SRB [27], Condor [22] and others) have provided fundamental building blocks, but there is an immense amount of work that remains to be done Current efforts in the commercial sector by IBM on Autonomic Computing , as discussed in 13, provide an exciting current focus likely to have a strong impact on the Grid Through Project Eliza , IBM is exploring the concepts of software which is self-optimizing, self-con guring, self-healing and self-protecting to.

This extends to the issue of digital rights management in making content available Metadata and annotation: Whilst the basic metadata infrastructure already exists in the shape of RDF, metadata issues have not been fully addressed in current Grid deployments It is relatively straightforward to deploy some of the technology in this area, and this should be promoted RDF, for example, is already encoding metadata and annotations as shared vocabularies or ontologies However, there is still a need for extensive work in the area of tools and methods to support the design and deployment of e-Science ontologies Annotation tools and methods need to be developed so that emerging metadata and ontologies can be applied to the large amount of content that will be present in Grid applications.

These functions compute statistics on repeating and nonrepeating (normal) fields. The result can be either repeating or nonrepeating. Although the arguments are often Number fields, Date and Time fields can also be used. The aggregate functions are as follows: Average Count Max

how to write pdf file in asp.net c#

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