Energy-efficient management is key in modern data centers in order to reduce
operational cost and environmental contamination. Energy management
and renewable energy utilization are strategies to optimize energy consumption
in high-performance computing. In any case, understanding the power consumption
behavior of physical servers in datacenter is fundamental to implement
energy-aware policies effectively. These policies should deal with possible
performance degradation of applications to ensure quality of service.
This thesis presents an empirical evaluation of power consumption for scientific
computing applications in multicore systems. Three types of applications
are studied, in single and combined executions on Intel and AMD servers, for
evaluating the overall power consumption of each application. The main results
indicate that power consumption behavior has a strong dependency with
the type of application. Additional performance analysis shows that the best
load of the server regarding energy efficiency depends on the type of the applications,
with efficiency decreasing in heavily loaded situations. These results
allow formulating models to characterize applications according to power consumption,
efficiency, and resource sharing, which provide useful information
for resource management and scheduling policies. Several scheduling strategies
are evaluated using the proposed energy model over realistic scientific computing
workloads. Results confirm that strategies that maximize host utilization
provide the best energy efficiency.Agencia Nacional de Investigación e Innovación FSE_1_2017_1_14478
