Network Bandwidth Usage Forecast in Content Delivery Networks

Aykut Teker; Ahmet Haydar Ornek; Berk Canberk

doi:10.1109/CoBCom49975.2020.9174180

Network Bandwidth Usage Forecast in Content Delivery Networks

Aykut Teker
, Ahmet Haydar Ornek
, Berk Canberk

Şişli/İ

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

Operational burden of a Content Delivery Network that is a vast overlay network on top of current Internet Architecture can be alleviated by forecasting Content Delivery Network bandwidths. The purpose of this paper is to forecast network bandwidth usage for Content Delivery Networks' Points of Presence. In this paper we compare Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks that can be used for predicting and minimizing operational costs of Content Delivery Networks via resource allocation, server allotment and local ISP bandwidth contract costs. We directly forecast end-user to Content Delivery Network bandwidth, so it can directly be used to lower end-user latencies. In this paper; we first conduct Self-Similarity Analysis and then utilize Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks to predict bandwidth usage with 6.338% error.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728174921
DOI'lar	https://doi.org/10.1109/CoBCom49975.2020.9174180
Yayın durumu	Yayınlandı - Tem 2020
Harici olarak yayınlandı	Evet
Etkinlik	3rd International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2020 - Graz, Austria Süre: 7 Tem 2020 → 9 Tem 2020

Yayın serisi

Adı	CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	3rd International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2020
Ülke/Bölge	Austria
Şehir	Graz
Periyot	7/07/20 → 9/07/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Belgeye Erişim

10.1109/CoBCom49975.2020.9174180

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Teker, A., Ornek, A. H., & Canberk, B. (2020). Network Bandwidth Usage Forecast in Content Delivery Networks. In CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings Makale 9174180 (CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoBCom49975.2020.9174180

Teker, Aykut ; Ornek, Ahmet Haydar ; Canberk, Berk. / Network Bandwidth Usage Forecast in Content Delivery Networks. CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings).

@inproceedings{211931acf51f412792fbd8f072a11cc4,

title = "Network Bandwidth Usage Forecast in Content Delivery Networks",

abstract = "Operational burden of a Content Delivery Network that is a vast overlay network on top of current Internet Architecture can be alleviated by forecasting Content Delivery Network bandwidths. The purpose of this paper is to forecast network bandwidth usage for Content Delivery Networks' Points of Presence. In this paper we compare Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks that can be used for predicting and minimizing operational costs of Content Delivery Networks via resource allocation, server allotment and local ISP bandwidth contract costs. We directly forecast end-user to Content Delivery Network bandwidth, so it can directly be used to lower end-user latencies. In this paper; we first conduct Self-Similarity Analysis and then utilize Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks to predict bandwidth usage with 6.338\% error.",

keywords = "artificial neural networks, content delivery networks, seasonal auto-regressive integrated moving averages, traffic modelling",

author = "Aykut Teker and Ornek, \{Ahmet Haydar\} and Berk Canberk",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2020 ; Conference date: 07-07-2020 Through 09-07-2020",

year = "2020",

month = jul,

doi = "10.1109/CoBCom49975.2020.9174180",

language = "English",

series = "CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings",

address = "United States",

}

Teker, A, Ornek, AH & Canberk, B 2020, Network Bandwidth Usage Forecast in Content Delivery Networks. in CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings., 9174180, CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings, Institute of Electrical and Electronics Engineers Inc., 3rd International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2020, Graz, Austria, 7/07/20. https://doi.org/10.1109/CoBCom49975.2020.9174180

Network Bandwidth Usage Forecast in Content Delivery Networks. / Teker, Aykut; Ornek, Ahmet Haydar; Canberk, Berk.
CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9174180 (CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Network Bandwidth Usage Forecast in Content Delivery Networks

AU - Teker, Aykut

AU - Ornek, Ahmet Haydar

AU - Canberk, Berk

PY - 2020/7

Y1 - 2020/7

N2 - Operational burden of a Content Delivery Network that is a vast overlay network on top of current Internet Architecture can be alleviated by forecasting Content Delivery Network bandwidths. The purpose of this paper is to forecast network bandwidth usage for Content Delivery Networks' Points of Presence. In this paper we compare Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks that can be used for predicting and minimizing operational costs of Content Delivery Networks via resource allocation, server allotment and local ISP bandwidth contract costs. We directly forecast end-user to Content Delivery Network bandwidth, so it can directly be used to lower end-user latencies. In this paper; we first conduct Self-Similarity Analysis and then utilize Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks to predict bandwidth usage with 6.338% error.

AB - Operational burden of a Content Delivery Network that is a vast overlay network on top of current Internet Architecture can be alleviated by forecasting Content Delivery Network bandwidths. The purpose of this paper is to forecast network bandwidth usage for Content Delivery Networks' Points of Presence. In this paper we compare Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks that can be used for predicting and minimizing operational costs of Content Delivery Networks via resource allocation, server allotment and local ISP bandwidth contract costs. We directly forecast end-user to Content Delivery Network bandwidth, so it can directly be used to lower end-user latencies. In this paper; we first conduct Self-Similarity Analysis and then utilize Seasonal Auto-Regressive Integrated Moving Averages and Artificial Neural Networks to predict bandwidth usage with 6.338% error.

KW - artificial neural networks

KW - content delivery networks

KW - seasonal auto-regressive integrated moving averages

KW - traffic modelling

UR - https://www.scopus.com/pages/publications/85092298024

U2 - 10.1109/CoBCom49975.2020.9174180

DO - 10.1109/CoBCom49975.2020.9174180

M3 - Conference contribution

AN - SCOPUS:85092298024

T3 - CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings

BT - CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 2020

Y2 - 7 July 2020 through 9 July 2020

ER -

Teker A, Ornek AH, Canberk B. Network Bandwidth Usage Forecast in Content Delivery Networks. In CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9174180. (CoBCom 2020 - International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications - Proceedings). doi: 10.1109/CoBCom49975.2020.9174180

Network Bandwidth Usage Forecast in Content Delivery Networks

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

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