Efficient data service design for a SOA approach to scientific computing

Abstract

In recent years, the Service Oriented Architecture (SOA) has evolved itself into emerging technologies like cloud computing to give it more relevance. ANU-SOAM - a service oriented middleware - aims to provide convenient API, a unique data service extension and proper load-balancing techniques for high performance scientific computing. The data service extension offers both Common Data Service (CDS) and Local Data Service (LDS). CDS helps set data common to all service instances and to manipulate it using add, get, put, sync, etc. functions. The LDS allows consumer to partially replicate data among service instances to improve memory scalability. Comparable paradigms like MPI are mostly agnostic and non-responsive to heterogeneous conditions. The SOA approach enables ANU-SOAM to have load balancing techniques implemented with the help of a Resource Manager. Experiments using N Body Solver and Heat Transfer applications have shown that ANU-SOAM performs as good as most of its MPI counterparts, especially under heterogeneous conditions.