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Adapting to Workload Changes Through On-The-Fly Reconfiguration.
Jonathan
Wildstrom, Peter Stone, Emmett
Witchel, and Mike Dahlin.
Technical Report UT-AI-TR-06-330, The University
of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2006.
At ftp://ftp.cs.utexas.edu/pub/AI-Lab/tech-reports/UT-AI-TR-06-330.pdf
(unavailable)
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 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 publically 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.
@TechReport(Wildstrom06-tech,
author="Jonathan Wildstrom and Peter Stone and Emmett Witchel and Mike Dahlin",
title="Adapting to Workload Changes Through On-The-Fly Reconfiguration",
Institution="The University of Texas at Austin, Department of Computer Sciences, AI Laboratory",
number="UT-AI-TR-06-330",
year="2006",month="June",
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 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 publically 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={At <a href="ftp://ftp.cs.utexas.edu/pub/AI-Lab/tech-reports/UT-AI-TR-06-330.pdf">ftp://ftp.cs.utexas.edu/pub/AI-Lab/tech-reports/UT-AI-TR-06-330.pdf</a>},
)
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