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Science Gateways today are generally built to provide a web-accessible interface for a particular scientific community to access a combination of software, hardware, and data deployed in an expertly managed computing center. But what happens when the scientist wants to repatriate their data? Or perform some analysis that is not supported by the gateway? Both for the purposes of encouraging innovative workflows and serving an audience with a wide range of computational experience it is important to consider how a gateway can fit into the broader computational ecosystem of a particular researcher or research group. One simple starting point for this is to ask the question "how can the gateway connect back to the laptop?". This talk will consider how this is being done today in science gateways and present some ideas for how this could be expanded in the future.

Current grid computing architectures have been based on cluster management and batch queuing systems, extended to a distributed, federated domain. These have shown shortcomings in terms of scalability, stability, and modularity. To address these problems, this dissertation applies architectural styles from the Internet and Web to the domain of generic computational grids. Using the REST style, a flexible model for grid resource interaction is developed which removes the need for any centralised services or specific protocols, thereby allowing a range of implementations and layering of further functionality. The context for resource interaction is a generalisation and formalisation of the Condor ClassAd match-making mechanism. This set theoretic model is described in depth, including the advantages and features which it realises. This RESTful style is also motivated by operational experience with existing grid infrastructures, and the design, operation, and performance of a proto-RESTful grid middleware package named DIRAC. This package was designed to provide for the LHCb particle physics experiment’s “off-line” computational infrastructure, and was first exercised during a 6 month data challenge which utilised over 670 years of CPU time and produced 98 TB of data through 300,000 tasks executed at computing centres around the world. The design of DIRAC and performance measures from the data challenge are reported. The main contribution of this work is the development of a REST model for grid resource interaction. In particular, it allows resource templating for scheduling queues which provide a novel distributed and scalable approach to resource scheduling on the grid.