A collaborative e-science architecture for distributed scientific communities
Modern scientific research problems are getting more and more complicated. Addressing these problems require knowledge and expertise from a wide range of scientific disciplines. The instruments required for modern scientific research problems are also complex and expensive. In addition, the amount of research data generated by experiments on these problems is getter bigger to an extent that might not be manageable by any individual organisations. All of these factors have made global distributed collaborations become increasingly important in modern scientific research. Dealing with distributed collaborations at such a large scale has given rise to a new subject called e-Science. Grids have been widely accepted as promising infrastructures for e-Science. Grids enable the sharing of large-scale computational resources and experimental datasets in distributed virtual organisations. Web-based collaborative portals are commonly used as environments for interactions amongst distributed collaborators. Collaborators in a Web-based environment are subject to certain level of centralised administration and control. Their interactions have to be routed through a central server. This has been seen as inflexible and does not scale well with respect to the heterogeneity of distributed user communities. This thesis reports an investigation on a Collaborative e-Science Architecture (CeSA), which is an integration of Grid and Peer-to-Peer computing infrastructures using service oriented architecture, for supporting distributed scientific collaborations. CeSA leverages the advantages of Peer-to-Peer computing in supporting direct collaborations amongst end users and the capability of providing large-scale computational resources and experimental datasets. The investigation addressed two important issues with regard to the CeSA: (i) usability of the CeSA from users' point of view and (ii) an efficient resource discovery mechanism for the Peer-to-Peer environment. The usability was evaluated using the reaction kinetic research group in Leeds as a case study. An instance of the CeSA was prototyped for the evaluation. Feedback collected from the users was positive. An adaptive resource discovery approach has been introduced for the P2P collaborative environment of the CeSA. This adaptive approach takes into account the resource distribution and characteristics of scientific research communities. A learning mechanism, based on a classification of user interests using ontology, is used to adaptively route search queries to peers which are most likely to have the answers. Simulation results showed that this approach can efficiently improve query hit rates and also scale well with the increasing of network populations.