@inproceedings{cybenko:grid,
    author = {George Cybenko and Guofei Jiang and Daniel Bilar},
    title = {Machine Learning Applications in Grid Computing},
    booktitle = {Proceedings of the 37th Allerton Conference on Communication,
Control and Computing},
    year = {1999},
    month = {September},
    pages = {348--357},
    copyright = {Allerton Conference},
    group = {agents, actcomm, coabs},
    url = {http://agent.cs.dartmouth.edu/papers/cybenko:grid.ps.gz},
    keyword = {machine learning, WWW, grid computing, functional validation,
mobile agent},
    abstract = {The development of the World Wide Web has changed the way that we
think about information. Information on the web is distributed, updates are
made asynchronously and resources come and go online 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 in which computing and data resources are located
throughout a network. Grid computing is enabled by an infrastructure that
allows users to locate computing resources and data products dynamically
during a computation. In order to locate resources dynamically in a grid
computation, a grid application program consults a broker or matchmaker agent
that uses keywords and ontologies to specify grid services. However, we
believe that keywords and ontologies cannot be defined or interpreted
precisely enough to make brokering and matchmaking between agents
sufficiently robust in a truly distributed, heterogeneous computing
environment. To this end, we introduce the concept of functional validation.
Functional validation goes beyond the symbolic negotiation level of brokering
and matchmaking, to the level of validating actual functional performance of
grid services. In this paper, we present the functional validation problem in
grid computing and apply basic machine learning theory such as PAC learning
and Chernoff bounds to solve the sample size problem that arises.
Furthermore, in order to reduce network traffic and speedup the validation
process, we describe the use of Dartmouth D'Agents technology to implement a
general mobile functional validation agent system which can be integrated
into a grid computing infrastructures as a standard grid service.}
}