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@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>},
)