Enabling a Mobility Prediction-aware Follow-Me Cloud Model

Bruno Sousa; Zhongliang Zhao; Morteza  Karimzadeh; David Palma; Vitor Fonseca; Paulo Simoes; Torsten Braun; Hans van den Berg; Aiko Pras; Luis Cordeiro

doi:10.1109/LCN.2016.77

Enabling a Mobility Prediction-aware Follow-Me Cloud Model

Bruno Sousa
, Zhongliang Zhao
, Morteza Karimzadeh
, David Palma
, Vitor Fonseca
, Paulo Simoes
, Torsten Braun
, Hans van den Berg
, Aiko Pras
, Luis Cordeiro

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

9 Citations (Scopus)

200 Downloads (Pure)

Abstract

The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33% improvement of content retrieval time.

Original language	English
Title of host publication	Proceedings of the 41st IEEE Conference on Local Computer Networks (LCN 2016)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	486-494
Number of pages	9
ISBN (Electronic)	978-1-5090-2054-6
ISBN (Print)	978-1-5090-2055-3
DOIs	https://doi.org/10.1109/LCN.2016.77
Publication status	Published - Nov 2016
Event	41st IEEE Conference on Local Computer Networks, LCN 2016 - Dubai, United Arab Emirates Duration: 7 Nov 2016 → 10 Nov 2016 Conference number: 41

Conference

Conference	41st IEEE Conference on Local Computer Networks, LCN 2016
Abbreviated title	LCN
Country/Territory	United Arab Emirates
City	Dubai
Period	7/11/16 → 10/11/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 10 Reduced Inequalities

Keywords

2023 OA procedure

Access to Document

10.1109/LCN.2016.77

ArticleFinal published version, 1.71 MBLicence: Taverne

Cite this

@inproceedings{00a5f2445e55417a993db4c653910cfa,

title = "Enabling a Mobility Prediction-aware Follow-Me Cloud Model",

abstract = "The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33\% improvement of content retrieval time.",

keywords = "2023 OA procedure",

author = "Bruno Sousa and Zhongliang Zhao and Morteza Karimzadeh and David Palma and Vitor Fonseca and Paulo Simoes and Torsten Braun and \{van den Berg\}, Hans and Aiko Pras and Luis Cordeiro",

year = "2016",

month = nov,

doi = "10.1109/LCN.2016.77",

language = "English",

isbn = "978-1-5090-2055-3",

pages = "486--494",

booktitle = "Proceedings of the 41st IEEE Conference on Local Computer Networks (LCN 2016)",

publisher = "IEEE",

address = "United States",

note = "41st IEEE Conference on Local Computer Networks, LCN 2016, LCN ; Conference date: 07-11-2016 Through 10-11-2016",

}

Sousa, B, Zhao, Z, Karimzadeh, M, Palma, D, Fonseca, V, Simoes, P, Braun, T, van den Berg, H, Pras, A & Cordeiro, L 2016, Enabling a Mobility Prediction-aware Follow-Me Cloud Model. in Proceedings of the 41st IEEE Conference on Local Computer Networks (LCN 2016). IEEE, Piscataway, NJ, pp. 486-494, 41st IEEE Conference on Local Computer Networks, LCN 2016, Dubai, United Arab Emirates, 7/11/16. https://doi.org/10.1109/LCN.2016.77

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T1 - Enabling a Mobility Prediction-aware Follow-Me Cloud Model

AU - Sousa, Bruno

AU - Zhao, Zhongliang

AU - Karimzadeh, Morteza

AU - Palma, David

AU - Fonseca, Vitor

AU - Simoes, Paulo

AU - Braun, Torsten

AU - van den Berg, Hans

AU - Pras, Aiko

AU - Cordeiro, Luis

N1 - Conference code: 41

PY - 2016/11

Y1 - 2016/11

N2 - The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33% improvement of content retrieval time.

AB - The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33% improvement of content retrieval time.

KW - 2023 OA procedure

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M3 - Conference contribution

SN - 978-1-5090-2055-3

SP - 486

EP - 494

BT - Proceedings of the 41st IEEE Conference on Local Computer Networks (LCN 2016)

PB - IEEE

CY - Piscataway, NJ

T2 - 41st IEEE Conference on Local Computer Networks, LCN 2016

Y2 - 7 November 2016 through 10 November 2016

ER -

Enabling a Mobility Prediction-aware Follow-Me Cloud Model

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this