Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks

Zhao, Zhongliang; Karimzadeh, Morteza; Braun, Torsten; Pras, Aiko; Berg, Hans (May 2017). Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks. In: IEEE/IFIP International Symposium on Integrated Network Management (IM 2017). IEEE 10.23919/INM.2017.7987321

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Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to request the prediction information on-demand and respond accordingly. In this paper we introduce MOBaaS, which is a network function of Mobility and Bandwidth prediction cloudified over the cloud computing infrastructure. We implemented the service orchestration framework of MOBaaS, which can easily be setup and integrated with any other cloud-based LTE entities to provide prediction information about the future location of mobile user(s) as well as the network link(s) bandwidth availability. This information can be used to generate required triggers for on-demand deployment or scaling-up/down of virtualized network components as well as for the self-adaptation procedures and optimal network function configuration. We also describe the performance evaluation of the MOBaaS cloudification procedures and present an example of the benefit of such a prediction service.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS) 08 Faculty of Science > Institute of Computer Science (INF)
UniBE Contributor:	Zhao, Zhongliang, Braun, Torsten
Subjects:	000 Computer science, knowledge & systems 500 Science > 510 Mathematics
Publisher:	IEEE
Language:	English
Submitter:	Dimitrios Xenakis
Date Deposited:	21 Nov 2016 16:37
Last Modified:	05 Dec 2022 15:00
Publisher DOI:	10.23919/INM.2017.7987321
URI:	https://boris.unibe.ch/id/eprint/90476