@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.90
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings(ICAC05,
        author="Jonathan Wildstrom and Peter Stone and Emmett Witchel and Raymond J. Mooney and Mike Dahlin",
        title="Towards Self-Configuring Hardware for Distributed Computer Systems",
        booktitle="The Second International Conference on Autonomic Computing",
       month="June",year="2005",
       pages="241--249",
       abstract={
                 High-end servers that can be partitioned into logical
                 subsystems and repartitioned on the fly are now
                 becoming available.  This development raises the
                 possibility of reconfiguring distributed systems
                 online to optimize for dynamically changing
                 workloads. This paper presents the initial steps
                 towards a system that can learn to alter its current
                 configuration in reaction to the current workload.
                 In particular, the advantages of shifting CPU and
                 memory resources online are considered.
                 Investigation on a publically available
                 multi-machine, multi-process distributed system (the
                 online transaction processing benchmark TPC-W)
                 indicates that there is a real performance benefit to
                 reconfiguration in reaction to workload changes. A
                 learning framework is presented that does not require
                 any instrumentation of the middleware, nor any
                 special instrumentation of the operating system;
                 rather, it learns to identify preferable
                 configurations as well as their quantitative
                 performance effects from system behavior as reported
                 by standard monitoring tools.  Initial results using
                 the WEKA machine learning package suggest that
                 automatic adaptive configuration can provide
                 measurable performance benefits over any fixed
                 configuration.
                 },
       wwwnote={<a href="http://www.autonomic-conference.org">ICAC-05</a><br> A revised version of the paper appeared on IBM's <a href="http://www-128.ibm.com/developerworks/autonomic/library/ac-selfhw/">Developer Works</a> website},
)