Abstract
Rising costs of energy consumption and an ongoing
effort for increases in computing performance are leading
to a significant need for energy-efficient computing. Before
systems such as supercomputers, servers, and datacenters can
begin operating in an energy-efficient manner, the energy consumption
and performance characteristics of the system must
be analyzed. In this paper, we provide an analysis framework
that will allow a system administrator to investigate the tradeoffs
between system energy consumption and utility earned by
a system (as a measure of system performance). We model
these trade-offs as a bi-objective resource allocation problem.
We use a popular multi-objective genetic algorithm to construct
Pareto fronts to illustrate how different resource allocations can
cause a system to consume significantly different amounts of
energy and earn different amounts of utility. We demonstrate
our analysis framework using real data collected from online
benchmarks, and further provide a method to create larger
data sets that exhibit similar heterogeneity characteristics to
real data sets. This analysis framework can provide system
administrators with insight to make intelligent scheduling
decisions based on the energy and utility needs of their systems.
