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We Didn’t Start the Fire: Using Agent-Directed Thermal Modeler to Keep
Servers Cool
J. Hood, T. Scott, J. Yu, X. Qin*, L. Yilmaz,
and J. A. Hamilton
Computer Science and Software Engineering
Auburn University, Auburn, AL
36849
{ fjwh0011, tas0015,
jzy0012, xqin, yilmale}@eng.auburn.edu
As energy use by datacenters has risen over the years, the costs
required to run a datacenter have substantially increased. Several algorithms
for thermal management and thermal-aware job placement exist; however,
choosing the scheme that will most efficiently cool a datacenter can be
challenging. Thermal models offer a great solution to help choose which
algorithm will perform best by juxtaposing different thermal-aware
algorithms. When the temperature can be observed over all servers through
simulated steps, one can decipher the differences and advantages of one
thermal-aware algorithm over another. Existing thermal modelers, however, can
be slow and may take a while to learn to use. When one wishes to compare
several thermal models, waiting for hours for the result of one thermal-aware
algorithm may mean that not as many algorithms can be compared.
Agent-Directed Thermal Modeler (ADTM) provides a solution that has a low
learning curve and still produces visualizations of datacenters quickly. It
is aimed at being easy to configure while still producing very meaningful
data through graphs and images. There are very few parameters, making setup
much easier - one simply has to configure a few settings such as initial
number of jobs, job gain, and which thermal-aware algorithm to use. Once
these settings are configured, the simulation can be run in a matter of
seconds. A single time-step of the simulation takes milliseconds. The
graphical and pictorial output of ADTM can then be used to determine which
thermal-aware algorithm works best for a given datacenter in a much shorter
time than other thermal-modelers. ADTM is used to compare XInt-GA
to random job placement in order to show that a 5% increase in energy savings
is expected in an overloaded datacenter. This simulation for both algorithms
takes only a few seconds, so many thermal-aware algorithms can be compared
quickly in order to determine the most effective and realistic algorithm that
can be chosen to cool a datacenter.
This paper appeared in the Proceedings of the Agent-Directed Simulation
Symposium of the SpringSim Multiconference,
Tampa, Florida, April 2014.
*Corresponding author. http://www.eng.auburn.edu/~xqin
Acknowledgments: This
research was supported by the U.S. National Science Foundation under Grants
CCF-0845257 (CAREER), CNS-0917137 (CSR), CNS-0757778 (CSR), CCF-0742187
(CPA), CNS-0831502 (CyberTrust), CNS-0855251 (CRI),
OCI-0753305 (CI-TEAM), DUE-0837341 (CCLI), and DUE-0830831 (SFS).
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