RT Dissertation/Thesis
T1 Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
A1 Márquez Colás, María Cristina
AB Several business are nowadays becoming more and more aware of thepotential that lies beneath Big Data. From social media ‘titans’ andhealthcare companies, to the mobile industry that propelled them, 5thGeneration mobile network (5G) will be a fundamental factor that willdrive our new reality. Current mobile service usage has been exploredfor data-driven organizational growth in the touristic sector, as it allowsforecasting hotel occupancy rates or targeting customersHowever, mobile datais continuously increasing, and therefore it is enormously important to analyzesuch data for networking purposes.Previous Ericsson Mobility Reports claimed that by the end of 2022 thetotal monthly traffic associated with mobile devices would be 77 exabytes(EB), representing 20% of the total Internet Protocol (IP) traffic aroundthe world. They also declare that 50 EB/month would come from 2ndGeneration mobile network (2G), 3rd Generation mobile network (3G), and4th Generation mobile network (4G) devices. In terms of 5G subscriptions,it is expected to reach up to 2.8 billion subscriptions globally by the end of2025, accounting for about 30% of total mobile subscriptions. Experts stakeout that by the year 2020, 1.7 megabytes of data will be generated every secondfor every person on the planet, forcing the network to evolve and adapt tochallenging new demands.Network providers not only deal with the deployment of the requiredresources to support this growth, but also the potential newcomers into thebusiness, and the stringent conditions of the distinct services to be provided.One of the features proposed to face this dilemma is using the network slicingtechnique. It allows to transform and orchestrate a 5G network by creatingmultiple logical instances (i.e., slices) on top of it, while Big Data wouldprovide the specifications of the services’ traffic dynamics to be served. Inthis way, operators achieve the best allocation of resources.This thesis contributes to the ongoing Network Slicing research, assessinga nationwide scenario. Our results show mobile traffic similarities anddifferences across time, space, and frequency domains, whereas we intendfor distinct service clusterizations that would enhance the network efficiencyin terms of resource management. For instance, we show that benefits areachieved when considering the top 10 consuming Network Slice (NS). Inaddition, we could observe mobile service similarities in the spatial domain,while the spectral and time domains open the door for wavelets uncertainty,where we point future directions to address this research branch. Moreover,we propose two data-driven algorithms that shed light on the trade-offbetween complexity and multiplexing efficiency derived from the network slicespecifications, both exhibiting promising performances (e.g., leading to a newarchitecture for traffic balancing in the cloud and edge clusters, with 60% and400% gain in efficiency respectively and 1/3 of dedicated resources).
YR 2020
FD 2020-11-27
LK https://hdl.handle.net/10016/32301
UL https://hdl.handle.net/10016/32301
LA eng
NO Mención Internacional en el título de doctor
DS e-Archivo
RD 27 jul. 2024