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 servers, 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. The authors demonstrate a methodology to facilitate collaborative scientific computing. Their idea is comprised 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. The techniques work in an on-line manner and cater to mutually non-exclusive classes which are critical in the collaborative networked computing landscape