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

1 Citation (Scopus)

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
Publication status	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 2022 Oct 19 → 2022 Oct 21

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	22/10/19 → 22/10/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102), and the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2022-2020-0-01816) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).

Publisher Copyright:
© 2022 IEEE.

Keywords

5G/6G networks
traffic prediction
transfer learning

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

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

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

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, 22/10/19. https://doi.org/10.1109/ICTC55196.2022.9952575

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

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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 = "Funding Information: ACKNOWLEDGMENT This research was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102), and the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2022-2020-0-01816) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). 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",

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TransTraffic: Predicting Network Traffic using Low Resource Data

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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