Lab Grids

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Historically the term grid has been used to describe a worldwide communication infrastructure for clustered computers that allows seamless transparent access to data and computing power on demand in order to solve large scale computational problems. Such computing grids cost a fraction of what a supercomputer costs. They are commonly known in engineering, science and commerce.

Grid is also a new paradigm for the information technology. The well-known World Wide Web will be succeeded by the upcoming World Wide Grid. Futurologists are promising that it will be possible to get large IT-resources “from a plug in the wall” without needing to know who provides the resources and where the resources are coming from. Nowadays, such service-oriented grids find applications in quite new areas not previously considered as the environments for a grid. An example of one such area is education. [15]

In the LOs (learning objects) concept, the learning content is split into reusable elements. These elements are used to build complex learning resources. In the world of service oriented grids the LOs are becoming fully functional services with their own user interface. They are independently interoperable blocks, which may be used as they are, or reused to build new and more complex blocks using other grid services, e.g. orchestration. LOs themselves can be nested.

When delivering a nested LO for an experiment, several components are necessary and each of them is implemented as a separate LO. The required components would be: the LO rendering the experiment environment, the LO displaying an Excel worksheet to evaluate results and a scope LO displaying the experiment signal histories. These components would be embedded into another LO; therefore constituting a new composed unit called for example Experimenting 1, which itself could be nested in a more general LO. Due to the well-defined grid standards, the learning courses can be built from LOs delivered by different grid services. The grid techniques make cataloguing and the easy managing of Los possible using metadata. Metadata used to describe LOs and ontologies for the semantic modeling of the learning domain can be used to build and execute distributed learning applications on a Learning grid [15].