TransTraffic: Predicting Network Traffic using Low Resource Data

Chaewon Kang; Jeewoo Yoon; Daejin Choi; Eunil Park; Sangheon Pack; Jinyoung Han

doi:10.1109/ICTC55196.2022.9952575

TransTraffic: Predicting Network Traffic using Low Resource Data

Chaewon Kang
, Jeewoo Yoon
, Daejin Choi
, Eunil Park
, Sangheon Pack
, Jinyoung Han

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In private 5G/6G networks, an adequate and accurate resource management is essential. In this paper, we propose a traffic prediction model, TransTraffic, that utilizes transfer learning for low resource data. Our evaluation demonstrates that leveraging prior knowledge from a similar traffic domain helps predict network traffic for a new domain or service.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	786-788
Number of pages	3
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952575
State	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 19 Oct 2022 → 21 Oct 2022

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

5G/6G networks
traffic prediction
transfer learning

Access to Document

10.1109/ICTC55196.2022.9952575

Cite this

Kang, C., Yoon, J., Choi, D., Park, E., Pack, S., & Han, J. (2022). TransTraffic: Predicting Network Traffic using Low Resource Data. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 786-788). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952575

@inproceedings{009e4bd319a34b2aae3284ea8b64599f,

title = "TransTraffic: Predicting Network Traffic using Low Resource Data",

abstract = "In private 5G/6G networks, an adequate and accurate resource management is essential. In this paper, we propose a traffic prediction model, TransTraffic, that utilizes transfer learning for low resource data. Our evaluation demonstrates that leveraging prior knowledge from a similar traffic domain helps predict network traffic for a new domain or service.",

keywords = "5G/6G networks, traffic prediction, transfer learning",

author = "Chaewon Kang and Jeewoo Yoon and Daejin Choi and Eunil Park and Sangheon Pack and Jinyoung Han",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",

year = "2022",

doi = "10.1109/ICTC55196.2022.9952575",

language = "English",

series = "International Conference on ICT Convergence",

publisher = "IEEE Computer Society",

pages = "786--788",

booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",

}

Kang, C, Yoon, J, Choi, D, Park, E, Pack, S & Han, J 2022, TransTraffic: Predicting Network Traffic using Low Resource Data. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 786-788, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 19/10/22. https://doi.org/10.1109/ICTC55196.2022.9952575

TransTraffic: Predicting Network Traffic using Low Resource Data. / Kang, Chaewon; Yoon, Jeewoo; Choi, Daejin et al.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 786-788 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kang, Chaewon

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AU - Park, Eunil

AU - Pack, Sangheon

AU - Han, Jinyoung

PY - 2022

Y1 - 2022

N2 - In private 5G/6G networks, an adequate and accurate resource management is essential. In this paper, we propose a traffic prediction model, TransTraffic, that utilizes transfer learning for low resource data. Our evaluation demonstrates that leveraging prior knowledge from a similar traffic domain helps predict network traffic for a new domain or service.

AB - In private 5G/6G networks, an adequate and accurate resource management is essential. In this paper, we propose a traffic prediction model, TransTraffic, that utilizes transfer learning for low resource data. Our evaluation demonstrates that leveraging prior knowledge from a similar traffic domain helps predict network traffic for a new domain or service.

KW - 5G/6G networks

KW - traffic prediction

KW - transfer learning

UR - https://www.scopus.com/pages/publications/85143254387

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DO - 10.1109/ICTC55196.2022.9952575

M3 - Conference contribution

AN - SCOPUS:85143254387

T3 - International Conference on ICT Convergence

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BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence

PB - IEEE Computer Society

Y2 - 19 October 2022 through 21 October 2022

ER -

Kang C, Yoon J, Choi D, Park E, Pack S, Han J. TransTraffic: Predicting Network Traffic using Low Resource Data. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 786-788. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952575

TransTraffic: Predicting Network Traffic using Low Resource Data

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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Cite this