Peter Stone's Selected Publications

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Adaptive Job Routing and Scheduling

Shimon Whiteson and Peter Stone. Adaptive Job Routing and Scheduling. Engineering Applications of Artificial Intelligence, 17(7):855–69, October 2004. Special issue on Autonomic Computing and Automation
Available from the publisher's webpage
The version from this page corrects a minor error in the published version.
An earlier version appeared in the proceedings of The Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI 2004)

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Abstract

Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have begun proposing that computer systems be imbued with the ability to configure themselves, diagnose failures, and ultimately repair themselves in response to these failures. However, despite convincing arguments that such a shift would be desirable, as of yet there has been little concrete progress made towards this goal. We view these problems as fundamentally machine learning challenges. Hence, this article presents a new network simulator designed to study the application of machine learning methods from a system-wide perspective. We also introduce learning-based methods for addressing the problems of job routing and CPU scheduling in the networks we simulate. Our experimental results verify that methods using machine learning outperform reasonable heuristic and hand-coded approaches on example networks designed to capture many of the complexities that exist in real systems.

BibTeX Entry

@Article(EAAI04,
        author="Shimon Whiteson and Peter Stone",
        title="Adaptive Job Routing and Scheduling",
        journal="Engineering Applications of Artificial Intelligence",
	note="Special issue on Autonomic Computing and Automation",
        volume="17",number="7",
        pages="855--69",
        month="October",
        year="2004",
        abstract={
                  Computer systems are rapidly becoming so complex
                  that maintaining them with human support staffs will
                  be prohibitively expensive and inefficient.  In
                  response, visionaries have begun proposing that
                  computer systems be imbued with the ability to
                  configure themselves, diagnose failures, and
                  ultimately repair themselves in response to these
                  failures.  However, despite convincing arguments
                  that such a shift would be desirable, as of yet
                  there has been little concrete progress made towards
                  this goal.  We view these problems as fundamentally
                  \emph{machine learning} challenges.  Hence, this
                  article presents a new network simulator designed to
                  study the application of machine learning methods
                  from a system-wide perspective.  We also introduce
                  learning-based methods for addressing the problems
                  of job routing and CPU scheduling in the networks we
                  simulate.  Our experimental results verify that
                  methods using machine learning outperform reasonable
                  heuristic and hand-coded approaches on example
                  networks designed to capture many of the
                  complexities that exist in real systems.
        },
        wwwnote={
Available from the <a href="http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V2M-4DGW3D2-1&_user=108429&_handle=B-WA-A-W-AV-MsSAYZW-UUW-AAUEABZWYE-AAUZDADUYE-YDBCBCYVZ-AV-U&_fmt=full&_coverDate=10%2F01%2F2004&_rdoc=13&_orig=browse&_srch=%23toc%235706%232004%23999829992%23530554!&_cdi=5706&view=c&_acct=C000059713&_version=1&_urlVersion=0&_userid=108429&md5=dc3e878e117abe3ed6d8347340800824">publisher's webpage</a><br>
The version from this page corrects a minor error in the published version.<br>
An earlier version appeared in the proceedings of <a href="http://www.aaai.org/Conferences/IAAI/iaai04.php">The Sixteenth Innovative Applications of Artificial Intelligence Conference</a> (IAAI 2004)},
)

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