2018
1.
Nadig, D.; Jung, E.; Kettimuthu, R.; Foster, I.; Rao, S. V. Nageswara; Ramamurthy, B.
Comparative Performance Evaluation of High-performance Data Transfer Tools Proceedings Article
In: 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–6, 2018.
Abstract | BibTeX | Tags: Data transfer, Local area networks, Protocols, Reliability, Software architecture, Tools, Wide area networks | Links:
@inproceedings{nadig_comparative_2018,
title = {Comparative Performance Evaluation of High-performance Data Transfer Tools},
author = {D. Nadig and E. Jung and R. Kettimuthu and I. Foster and S. V. Nageswara Rao and B. Ramamurthy},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Nadig-et-al.-2018-Comparative-Performance-Evaluation-of-High-perform.pdf},
doi = {10.1109/ANTS.2018.8710071},
year = {2018},
date = {2018-12-01},
urldate = {2018-12-01},
booktitle = {2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)},
pages = {1--6},
abstract = {Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools’ performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.},
keywords = {Data transfer, Local area networks, Protocols, Reliability, Software architecture, Tools, Wide area networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools’ performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.