  With the proliferation of data movement across the Internet, global data
traffic per year has already exceeded the Zettabyte scale. The network
infrastructure and end-systems facilitating the vast data movement consume an
extensive amount of electricity, measured in terawatt-hours per year. This
massive energy footprint costs the world economy billions of dollars partially
due to energy consumed at the network end-systems. Although extensive research
has been done on managing power consumption within the core networking
infrastructure, there is little research on reducing the power consumption at
the end-systems during active data transfers. This paper presents a novel
cross-layer optimization framework, called Cross-LayerHLA, to minimize energy
consumption at the end-systems by applying machine learning techniques to
historical transfer logs and extracting the hidden relationships between
different parameters affecting both the performance and resource utilization.
It utilizes offline analysis to improve online learning and dynamic tuning of
application-level and kernel-level parameters with minimal overhead. This
approach minimizes end-system energy consumption and maximizes data transfer
throughput. Our experimental results show that Cross-LayerHLA outperforms other
state-of-the-art solutions in this area.
