loading
Papers

Research.Publish.Connect.

Paper

Authors: Naoki Ohzeki ; Ryo Yamamoto ; Satoshi Ohzahata and Toshihiko Kato

Affiliation: Graduate School of Informatics and Engineering, University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585 and Japan

ISBN: 978-989-758-378-0

Keyword(s): Tcp, Congestion Control, Passive Monitoring, Congestion Window, Recurrent Neural Network.

Related Ontology Subjects/Areas/Topics: Data Communication Networking ; Network Architectures ; Network Monitoring and Control ; Network Protocols ; Telecommunications

Abstract: Recently, as various types of networks are introduced, a number of TCP congestion control algorithms have been adopted. Since the TCP congestion control algorithms affect traffic characteristics in the Internet, it is important for network operators to analyse which algorithms are used widely in their backbone networks. In such an analysis, a lot of TCP flows need to be handled and so the automatically processing is indispensable. Thin paper proposes a machine learning based method for estimating TCP congestion control algorithms. The proposed method uses a passively collected packet traces including both data and ACK segments, and calculates a time sequence of congestion window size for individual TCP flows contained in the trances. We use s recurrent neural network based classifier in the congestion control algorithm estimation. As the results of applying the proposed classifier to ten congestion control algorithms, the major three algorithms were clearly classified from the packet traces, and ten algorithms could be categorized into several groups which have similar characteristics. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.232.51.247

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ohzeki, N.; Yamamoto, R.; Ohzahata, S. and Kato, T. (2019). Estimating TCP Congestion Control Algorithms from Passively Collected Packet Traces using Recurrent Neural Network.In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 1: DCNET, ISBN 978-989-758-378-0, pages 27-36. DOI: 10.5220/0007916200270036

@conference{dcnet19,
author={Naoki Ohzeki. and Ryo Yamamoto. and Satoshi Ohzahata. and Toshihiko Kato.},
title={Estimating TCP Congestion Control Algorithms from Passively Collected Packet Traces using Recurrent Neural Network},
booktitle={Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 1: DCNET,},
year={2019},
pages={27-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007916200270036},
isbn={978-989-758-378-0},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 1: DCNET,
TI - Estimating TCP Congestion Control Algorithms from Passively Collected Packet Traces using Recurrent Neural Network
SN - 978-989-758-378-0
AU - Ohzeki, N.
AU - Yamamoto, R.
AU - Ohzahata, S.
AU - Kato, T.
PY - 2019
SP - 27
EP - 36
DO - 10.5220/0007916200270036

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.