Functional validation in grid computing

The development of the World Wide Web has changed the way we think about information. Information on the web is distributed, updates are made asynchronously and resources come online and go offline without centralized control. Global networking will similarly change the way we think about and perform computation. Grid computing refers to computing in a distributed networked environment where computing and data resources are located throughout a network. In order to locate these resources dynamically in a grid computation, a broker or matchmaker uses keywords and ontologies to describe and specify grid services. However, we believe that keywords and ontologies can not always be defined or interpreted precisely enough to achieve deep semantic agreement in a truly distributed, heterogeneous computing environment. To this end, we introduce the concept of functional validation. Functional validation goes beyond the symbolic level of brokering and matchmaking, to the level of validating actual functional performance of grid services. In this paper, we present the functional validation concept in grid computing, analyze the possible validation situations and apply basic machine learning theory such as PAC learning and Chernoff bounds to explore the relationship between sample size and confidence in service semantics.