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Jonathan Wildstrom, Peter Stone,
and Emmett Witchel. Autonomous Return on Investment Analysis of Additional
Processing Resources. In 2007 Workshop on Adaptive Methods in Autonomic Computing
Systems, June 2007.
AMACS-07
[PDF]154.7kB [postscript]275.5kB
As the use of virtualization 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
@InProceedings(AMACS07-jon,
author="Jonathan Wildstrom and Peter Stone and Emmett Witchel",
title="Autonomous Return on Investment Analysis of Additional
Processing Resources",
BookTitle="2007 Workshop on Adaptive Methods in Autonomic
Computing Systems",
month="June",year="2007",
abstract="
As the use of virtualization 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://http://research.ihost.com/amacs07/">AMACS-07</a>},
)
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