Work

An automated parameter optimizer for data transfer performance testing

Year: 2025

Type: article

Abstract: This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods. • A data transfer performance testing framework with both TCP and UDT support. • Automated fast parameter optimization using stochastic approximation. • Straightforward integration of additional data transfer protocols and alternative optimization methods.

Source: Software Impacts

DOI: 10.1016/j.simpa.2025.100764

Authors: Daqing Yun, Liudong Zuo, Yi Gu, Chase Wu

Institutions: California State University, Dominguez Hills, Middle Tennessee State University, New Jersey Institute of Technology

Language: English

Topic: Network Traffic and Congestion Control

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $600