The annual electricity consumed by the global data movement is estimated to be more than 200 terawatt-hours at the current rate, costing more than 40 billion U.S. dollars per year. GreenDataFlow project aims to reduce the energy footprint of the global data movement by (1) analyzing the energy vs. performance tradeoffs of end-system and protocol parameters during active data transfers; (2) investigating the accurate prediction of the network device power consumption due to increased data transfer rate on the active links and dynamic readjustment of the transfer rate to balance the energy over performance ratio; and (3) exploring service level agreement (SLA) based energy-efficient transfer algorithms, which will help the service providers to minimize the energy consumption during data transfers without compromising the SLA with the customer in terms of the promised performance level, but still execute the transfers with minimal energy levels given the requirements.