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Towards Self-Configuring Hardware for Distributed Computer Systems.
Jonathan
Wildstrom, Peter Stone, Emmett
Witchel, Raymond J. Mooney, and Mike
Dahlin.
In The Second International Conference on Autonomic Computing, pp. 241–249, June 2005.
ICAC-05
A revised version of the paper appeared on IBM's Developer
Works website
[PDF]74.0kB [postscript]104.6kB
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.
@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},
)
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