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@InProceedings{ICAC08,
author = "Jonathan Wildstrom and Peter Stone and Emmett
Witchel",
title = "CARVE: A Cognitive Agent for Resource Value
Estimation",
booktitle = "The Fifth International Conference on Autonomic
Computing",
month = "June",
year = 2008,
abstract = { Recently, industry has begun investigating and
moving towards utility computing, where
computational resources (processing, memory and I/O)
are availably on demand at a market cost. On-demand
access to computational resources enables
fine-grained resource allocation for web-based
applications, e.g., the possibility of provisioning
for a minimum workload while allowing the rental of
additional resources for unexpected workload
changes. However, renting additional resources
relies on the ability to quickly and accurately
estimate the value of the resource. This paper
introduces CARVE: a Cognitive Agent for Resource
Value Estimation. CARVE is a machine-learning based
approach that learns to predict the change in system
value of having more or less system resources. Using
only low-level statistics and with no custom
instrumentation of the operating system or
middleware, CARVE is able to make informed decisions
about the return on investment of physical memory
when implemented and evaluated on a partitioned
system running a multi-partition, multi-process
distributed benchmark. We show that CARVE is
competitive with static choices of computing
resources over a variety of test workloads and also
has the ability to outperform all static
configurations. },
note = "To appear.",
wwwnote = {ICAC-08},
}