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change over the next decade The future will expand existing technologies and integrate new technologies In the future, more resources will be linked by more and better networks At the end of the decade, sensors, PDAs, health monitors and other devices will be linked to the Grid Petabyte data resources and peta op computational resources will join low-level sensors and sensornets to constitute Grids of unprecedented heterogeneity and performance variation Over the next decade, Grid software will become more sophisticated, supporting unprecedented diversity, scale, globalization and adaptation Applications will use Grids in sophisticated ways, adapting to dynamic con gurations of resources and performance variations to achieve goals of Autonomic computing Accomplishing these technical and disciplinary achievements will require an immense research, development and deployment effort from the community Technical requirements will need to be supported by the human drivers for Grid research, development and education.

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].

All properties of the SliceInfo object are read-only, and are detailed in Table 26-8.

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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.

T(MeV)

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.

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

The publish operation of a registry has the signature void publish(XML tupleset). Within a tuple set, a tuple is uniquely identi ed by its tuple key, which is the pair (content link, context). If a key does not already exist on publication, a tuple is inserted into the registry database. An existing tuple can be updated by publishing other values under the same tuple key. An existing tuple (key) is owned by the content provider that created it with the rst publication. It is recommended that a content provider with another identity may not be permitted to publish or update the tuple.

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