Energy-Efficient Data Transfer Optimization via Decision-Tree Based Uncertainty Reduction

TL;DR

This paper introduces a decision-tree based model that optimizes data transfer throughput and reduces energy consumption at end systems, outperforming existing solutions through a two-phase offline and online approach.

Contribution

It presents a novel two-phase optimization model combining offline clustering and online search to enhance data transfer efficiency and energy savings.

Findings

117% higher throughput on average

19% less energy consumption at end systems

Outperforms state-of-the-art solutions

Abstract

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.