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Cloudified mobility and bandwidth prediction in virtualized LTE networks

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Author: Zhao, Z. · Karimzadeh, M. · Braun, T. · Pras, A. · Berg, J.L. van den
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source:Chemouil, P.Simoes, P.Madeira, E.Secci, S.Monteiro, E.Gaspary, L.P.Santos, C.R.P. dosCharalambides, M., 15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017. 8 May 2017 through 12 May 2017, 517-523
Identifier: 781359
Article number: 7987321
Keywords: Bandwidth · Distributed computer systems · Forecasting · Location based services · Transfer functions · Wireless telecommunication systems · Bandwidth availability · Bandwidth prediction · Cloud computing infrastructures · Cloud infrastructures · Industry standards · Network functions · Prediction informations · Service orchestration · Network function virtualization · 2016 ICT · CSR - Cyber Security & Robustness · TS - Technical Sciences


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. © 2017 IFIP. IEEE Communications Society; IFIP Working Group 6.6