Peter Stone's Selected Publications

Classified by TopicClassified by Publication TypeSorted by DateSorted by First Author Last NameClassified by Funding Source


Adapting to Workload Changes Through On-The-Fly Reconfiguration

Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin. Adapting to Workload Changes Through On-The-Fly Reconfiguration. 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

Download

(unavailable)

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.

BibTeX Entry

@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>},
)

Generated by bib2html.pl (written by Patrick Riley ) on Fri Sep 05, 2014 12:17:36