A machine learning approach for packet loss prediction in science flows-Reference-Cited by-同舟云学术

A machine learning approach for packet loss prediction in science flows

Published:2020-01 Issue: Volume:102 Page:190-197
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Giannakou Anna,Dwivedi Dipankar,Peisert Sean

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference26 articles.

1. The science DMZ: A network design pattern for data-intensive science;Dart,2013

2. B. Tierney, J. Metzger, J. Boote, E. Boyd, A. Brown, R. Carlson, M. Zekauskas, J. Zurawski, M. Swany, M. Grigoriev, perfSONAR: Instantiating a global network measurement framework.

3. On the predictability of large transfer TCP throughput;He,2005

4. A machine learning approach to TCP throughput prediction;Mirza,2007

5. B.A.A. Nunes, K. Veenstra, W. Ballenthin, S. Lukin, K. Obraczka, A Machine Learning Approach to End-to-End RTT Estimation and its Application to TCP, in: 2011 Proceedings of 20th International Conference on Computer Communications and Networks, ICCCN, 2011, pp. 1–6.

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