Stanojević, Rade and Shorten, Robert
Distributed dynamic speed scaling.
INFOCOM, 2010 Proceedings IEEE . ISBN 978-1-4244-5836-3 .
In recent years we have witnessed a great interest
in large distributed computing platforms, also known as clouds.
While these systems offer enormous computing power, they
are major energy consumers. In existing data centers CPUs
are responsible for approximately half of the energy consumed
by the servers. A promising technique for saving CPU energy
consumption is dynamic speed scaling, in which the speed at
which the processor is run is adjusted based on demand and
performance constraints. In this paper we look at the problem
of allocating the demand in the network of processors (each being
capable to perform dynamic speed scaling) to minimize the global
energy consumption/cost subject to a performance constraint.
The nonlinear dependence between the energy consumption and
the performance as well as the high variability in the energy
prices result in a nontrivial resource allocation. The problem
can be abstracted as a fully distributed convex optimization
with a linear constraint. On the theoretical side, we propose
two low-overhead fully decentralized algorithms for solving the
problem of interest and provide closed-form conditions that
ensure stability of the algorithms. Then we evaluate the efficacy
of the optimal solution using simulations driven by the real-world
energy prices. Our findings indicate a possible cost reduction of
10 − 40% compared to power-oblivious 1/N load balancing, for
a wide range of load factors.
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||Data center; energy management; distributed
coordination; dynamic speed scaling;
||Science & Engineering > Hamilton Institute
Dr. Robert Shorten
||10 Nov 2010 16:37
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||INFOCOM, 2010 Proceedings IEEE . ISBN 978-1-4244-5836-3
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