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Autonomous Return on Investment Analysis of Additional Processing Resources.
Jonathan
Wildstrom, Peter Stone, and Emmett
Witchel.
International Journal on Autonomic Computing, 1(3):280–296, Inderscience Publishers, Inderscience
Publishers, Geneva, SWITZERLAND, 2010.
IJAC
(unavailable)
As the use of virtualisation and partitioning grows, it becomes possible to deploy a multi-tier web-based application with a variable amount of computing power. This introduces the possibility of provisioning only for a minimum workload, with the intention of renting more resources as necessary, but it also creates the problem of quickly and accurately identifying when more resources are needed or unneeded resources are being paid for. This paper presents a machine learning based approach to handling this problem. An autonomous adaptive agent learns to predict the gain (or loss) that would result from more (or less) resources; this agent uses only low-level system statistics, rather than relying on custom instrumentation of the operating system or middleware. Our agent is fully implemented and evaluated on a publicly available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W). We show that our adaptive agent is competitive with any static choice of computing resources over a variety of test workloads. We also show that the agent outperforms each static choice in at least one case, implying that it is well suited for a situation where the workload is unknown.
@article{IJAC10,
author = {Jonathan Wildstrom and Peter Stone and Emmett Witchel},
title = {Autonomous Return on Investment Analysis of Additional Processing Resources},
journal = {International Journal on Autonomic Computing},
volume = {1},
number = {3},
year = {2010},
pages = {280--296},
doi = {http://dx.doi.org/10.1504/IJAC.2010.033010},
publisher = {Inderscience Publishers},
address = {Inderscience Publishers, Geneva, SWITZERLAND},
abstract="As the use of virtualisation and partitioning grows, it
becomes possible to deploy a multi-tier web-based
application with a variable amount of computing power. This
introduces the possibility of provisioning only for a
minimum workload, with the intention of renting more
resources as necessary, but it also creates the problem of
quickly and accurately identifying when more resources are
needed or unneeded resources are being paid for. This paper
presents a machine learning based approach to handling this
problem. An autonomous adaptive agent learns to predict the
gain (or loss) that would result from more (or less)
resources; this agent uses only low-level system
statistics, rather than relying on custom instrumentation
of the operating system or middleware. Our agent is fully
implemented and evaluated on a publicly available
multi-machine, multi-process distributed system (the online
transaction processing benchmark TPC-W). We show that our
adaptive agent is competitive with any static choice of
computing resources over a variety of test workloads. We
also show that the agent outperforms each static choice in
at least one case, implying that it is well suited for a
situation where the workload is unknown.",
wwwnote={<a href="http://www.inderscience.com/browse/index.php?journalCODE=ijac">IJAC</a>},
}
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