Peter Stone's Selected Publications

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Machine Learning for On-Line Hardware Reconfiguration

Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin. Machine Learning for On-Line Hardware Reconfiguration. In The 20th International Joint Conference on Artificial Intelligence, pp. 1113–1118, January 2007.
IJCAI-07

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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.

BibTeX Entry

@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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