  The increase and rapid growth of data produced by scientific instruments, the
Internet of Things (IoT), and social media is causing data transfer performance
and resource consumption to garner much attention in the research community.
The network infrastructure and end systems that enable this extensive data
movement use a substantial amount of electricity, measured in terawatt-hours
per year. Managing energy consumption within the core networking infrastructure
is an active research area, but there is a limited amount of work on reducing
power consumption at the end systems during active data transfers. This paper
presents a novel two-phase dynamic throughput and energy optimization model
that utilizes an offline decision-search-tree based clustering technique to
encapsulate and categorize historical data transfer log information and an
online search optimization algorithm to find the best application and kernel
layer parameter combination to maximize the achieved data transfer throughput
while minimizing the energy consumption. Our model also incorporates an
ensemble method to reduce aleatoric uncertainty in finding optimal application
and kernel layer parameters during the offline analysis phase. The experimental
evaluation results show that our decision-tree based model outperforms the
state-of-the-art solutions in this area by achieving 117% higher throughput on
average and also consuming 19% less energy at the end systems during active
data transfers.
