This work details the design and analysis of a fully decentralised scientific workflow management system for the execution of real applications in heterogeneous and unpredictable computing environments. Our model is based on a novel approach to the overlay metacomputer concept, founded on a graph-oriented peer-to-peer task distribution framework. This platform provides a basis for the development of a distributed virtual machine that can be dynamically deployed over a heterogeneous resource collection to provide a useful layer of abstraction, harnessing remote independent resources and making them appear dedicated to the execution of a particular collection of tasks. This paper demonstrates the utility of our model using synthetic and real workflow applications executed in a real and heterogeneous environment, identifying the potential benefit of fine-grained workflow expressions as a method for achieving predictable task execution behaviour in unpredictable environments.

The Geological Survey of Ireland (G.S.I) at present has over 4 terabytes of multi-beam sonar data gathered over the last five years from the seabed within the Irish designated zone as part of the Irish National Seabed Survey (INSS) and this data-set is expected to exceed 10 terabytes upon completion. Geological interpretation is carried out by visual inspection of bathymetric patterns. Due to the size of this data set and the emergence of similar data sets, the extraction of knowledge from such a data sets by human observers has become infeasible. The focus has turned to using artificial intelligence and computational methods for assistance. The commercial and environmental sensitivity of the data means that secure data processing and transmission are of paramount importance. This has lead to the creation of the MarineGrid project within the Grid Ireland organization. New method's have been developed for statistical analysis of bathymetric information specifically for automated geological interpretation of rock types on the sea floor and feature extraction from the sea floor. In this poster we present a brief synopsis of both a classification algorithm and a feature extraction algorithm and the results obtained from within the NUI Galway MarineGrid project.