# Energy-Efficient High-Throughput Data Transfers via Dynamic CPU Frequency and Core Scaling

## TL;DR

This paper presents three novel algorithms that dynamically tune CPU frequency and core usage to significantly reduce energy consumption and increase throughput during data transfers at the end systems.

## Contribution

It introduces new application-level parameter tuning algorithms using dynamic CPU scaling, outperforming existing solutions in energy efficiency and transfer speed.

## Key findings

- Up to 48% energy savings during data transfers.
- Achieved 80% higher throughput compared to state-of-the-art.
- Effective combination of heuristics and runtime measurements.

## Abstract

The energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. Depending on the number of switches, routers, and hubs between the source and destination nodes, the networking infrastructure consumes 10% - 75% of the total energy during active data transfers, and the rest is consumed by the end systems. Even though there has been extensive research on reducing the power consumption at the networking infrastructure, the work focusing on saving energy at the end systems has been limited to the tuning of a few application level parameters such as parallelism, pipelining, and concurrency. In this paper, we introduce three novel application-level parameter tuning algorithms which employ dynamic CPU frequency and core scaling, combining heuristics and runtime measurements to achieve energy efficient data transfers. Experimental results show that our proposed algorithms outperform the state-of-the-art solutions, achieving up to 48% reduced energy consumption and 80% higher throughput.