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@COMMENT written by Patrick Riley
@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={ICAC-05
A revised version of the paper appeared on IBM's Developer Works website},
)