Neuro-fuzzy approaches to collaborative scientific computing

Rapid advances in High Performance Computing (HPC) and the Internet are heralding a paradigm shift to network-based scientific software sewers, libraries, repositories and problem solving environments. According to this new paradigm, vital pieces of software and information required for a computation are distributed across a network and need to be identified and 'linked' together at run time; this implies a 'net-centric' and collaborative scenario for scientific computing. This scenario requires the application to dynamically choose the best among several competing resources that can solve a given problem. For these systems to become ubiquitous, efficient mechanisms for collaboration and automatic inference of the abilities of multiple 'compute servers' need to be established. In this paper we demonstrate a methodology to facilitate collaborative scientific computing. Our idea comprises of (i) a concept of 'reasonableness' to automatically generate exemplars for learning the mapping from problems to 'servers' and (ii) a neuro-fuzzy technique developed earlier by the authors that conducts supervised classification on the exemplars generated. Our techniques work in an on-line manner and cater to mutually non-exclusive classes which are critical in the collaborative networked computing landscape.