Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network

Syed M.A.H. Bukhari; Muhammad Afaq; Wang Cheol Song

doi:10.1109/ICUFN57995.2023.10200137

Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network

Syed M.A.H. Bukhari^*
, Muhammad Afaq
, Wang Cheol Song

^*Corresponding author for this work

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

4 Scopus citations

Abstract

With the advancement in network devices and the proliferation of new technologies such as Software-Defined Networking (SDN), managing a network becomes more difficult. In an SDN network, a single physical device acts as a firewall and load balancer at the same time. The management of those devices and the prevention of the resources being exhausted is a challenging task for the network administrator. In this direction, this paper presents an approach to predict resources on a switch in an SDN-based network. For this purpose, a video streaming scenario is deployed in an SDN network and performance metrics are captured. The resources are predicted using four machine learning algorithms. Specifically, the paper proposes a testbed implementation of a video streaming scenario to evaluate the performance of the proposed approach. The proposed approach can help network operators optimize network performance, ensure efficient use of resources, and enhance user experience.

Original language	English
Title of host publication	ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	596-601
Number of pages	6
ISBN (Electronic)	9798350335385
DOIs	https://doi.org/10.1109/ICUFN57995.2023.10200137
State	Published - 2023
Externally published	Yes
Event	14th International Conference on Ubiquitous and Future Networks, ICUFN 2023 - Paris, France Duration: 4 Jul 2023 → 7 Jul 2023

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2023-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	14th International Conference on Ubiquitous and Future Networks, ICUFN 2023
Country/Territory	France
City	Paris
Period	4/07/23 → 7/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

LSTM
SDN
XGBoost
time series prediction
video traffic prediction

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/ICUFN57995.2023.10200137

Cite this

Bukhari, S. M. A. H., Afaq, M., & Song, W. C. (2023). Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network. In ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks (pp. 596-601). (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2023-July). IEEE Computer Society. https://doi.org/10.1109/ICUFN57995.2023.10200137

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title = "Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network",

abstract = "With the advancement in network devices and the proliferation of new technologies such as Software-Defined Networking (SDN), managing a network becomes more difficult. In an SDN network, a single physical device acts as a firewall and load balancer at the same time. The management of those devices and the prevention of the resources being exhausted is a challenging task for the network administrator. In this direction, this paper presents an approach to predict resources on a switch in an SDN-based network. For this purpose, a video streaming scenario is deployed in an SDN network and performance metrics are captured. The resources are predicted using four machine learning algorithms. Specifically, the paper proposes a testbed implementation of a video streaming scenario to evaluate the performance of the proposed approach. The proposed approach can help network operators optimize network performance, ensure efficient use of resources, and enhance user experience.",

keywords = "LSTM, SDN, XGBoost, time series prediction, video traffic prediction",

author = "Bukhari, \{Syed M.A.H.\} and Muhammad Afaq and Song, \{Wang Cheol\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Ubiquitous and Future Networks, ICUFN 2023 ; Conference date: 04-07-2023 Through 07-07-2023",

year = "2023",

doi = "10.1109/ICUFN57995.2023.10200137",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

publisher = "IEEE Computer Society",

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Bukhari, SMAH, Afaq, M & Song, WC 2023, Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network. in ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2023-July, IEEE Computer Society, pp. 596-601, 14th International Conference on Ubiquitous and Future Networks, ICUFN 2023, Paris, France, 4/07/23. https://doi.org/10.1109/ICUFN57995.2023.10200137

Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network. / Bukhari, Syed M.A.H.; Afaq, Muhammad; Song, Wang Cheol.
ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2023. p. 596-601 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2023-July).

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

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Streaming via SDN: Resource forecasting for video streaming in a Software-Defined Network

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this