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Machine Learning for On-Line Hardware Reconfiguration.
Jonathan
Wildstrom, Peter Stone, Emmett
Witchel, and Mike Dahlin.
In The 20th International Joint Conference
on Artificial Intelligence, pp. 1113–1118, January 2007.
IJCAI-07
[PDF]98.6kB [postscript]191.7kB
As computer systems continue to increase in complexity, the need for AI-based solutions is becoming more urgent. For example, 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. However it also introduces the need to decide when and how to reconfigure. This paper presents one approach to solving this online reconfiguration problem. In particular, we learn to identify, from only low-level system statistics, which of a set of possible configurations will lead to better performance under the current unknown workload. This approach requires no instrumentation of the system's middleware or operating systems. We introduce an agent that is able to learn this model and use it to switch configurations online as the workload varies. Our agent is fully implemented and tested on a publicly available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W). We demonstrate that our adaptive configuration is able to outperform any single fixed configuration in the set over a variety of workloads, including gradual changes and abrupt workload spikes.
@InProceedings(IJCAI07-jon,
author="Jonathan Wildstrom and Peter Stone and Emmett Witchel and Mike Dahlin",
title="Machine Learning for On-Line Hardware Reconfiguration",
BookTitle="The 20th International Joint Conference on Artificial Intelligence",
month="January",year="2007",
pages="1113--1118",
abstract="
As computer systems continue to increase in
complexity, the need for AI-based solutions is
becoming more urgent. For example, 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. However it also
introduces the need to decide when and how to
reconfigure. This paper presents one approach to
solving this online reconfiguration problem. In
particular, we learn to identify, from only
low-level system statistics, which of a set of
possible configurations will lead to better
performance under the current unknown workload. This
approach requires no instrumentation of the system's
middleware or operating systems. We introduce an
agent that is able to learn this model and use it to
switch configurations online as the workload
varies. Our agent is fully implemented and tested on
a publicly available multi-machine, multi-process
distributed system (the online transaction
processing benchmark TPC-W). We demonstrate that
our adaptive configuration is able to outperform any
single fixed configuration in the set over a variety
of workloads, including gradual changes and abrupt
workload spikes.
",
wwwnote={<a href="http://www.ijcai-07.org/">IJCAI-07</a>},
)
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