Publication: Characterization of mobile network services to assess the impact of network slicing in a nationwide scenario
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2020-11-27
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2020-11-27
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Abstract
Several business are nowadays becoming more and more aware of the
potential that lies beneath Big Data. From social media ‘titans’ and
healthcare companies, to the mobile industry that propelled them, 5th
Generation mobile network (5G) will be a fundamental factor that will
drive our new reality. Current mobile service usage has been explored
for data-driven organizational growth in the touristic sector, as it allows
forecasting hotel occupancy rates or targeting customersHowever, mobile data
is continuously increasing, and therefore it is enormously important to analyze
such data for networking purposes.
Previous Ericsson Mobility Reports claimed that by the end of 2022 the
total monthly traffic associated with mobile devices would be 77 exabytes
(EB), representing 20% of the total Internet Protocol (IP) traffic around
the world. They also declare that 50 EB/month would come from 2nd
Generation mobile network (2G), 3rd Generation mobile network (3G), and
4th 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 of
2025, accounting for about 30% of total mobile subscriptions. Experts stake
out that by the year 2020, 1.7 megabytes of data will be generated every second
for every person on the planet, forcing the network to evolve and adapt to
challenging new demands.
Network providers not only deal with the deployment of the required
resources to support this growth, but also the potential newcomers into the
business, and the stringent conditions of the distinct services to be provided.
One of the features proposed to face this dilemma is using the network slicing
technique. It allows to transform and orchestrate a 5G network by creating
multiple logical instances (i.e., slices) on top of it, while Big Data would
provide the specifications of the services’ traffic dynamics to be served. In
this way, operators achieve the best allocation of resources.
This thesis contributes to the ongoing Network Slicing research, assessing
a nationwide scenario. Our results show mobile traffic similarities and
differences across time, space, and frequency domains, whereas we intend
for distinct service clusterizations that would enhance the network efficiency
in terms of resource management. For instance, we show that benefits are
achieved when considering the top 10 consuming Network Slice (NS). In
addition, 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-off
between complexity and multiplexing efficiency derived from the network slice
specifications, both exhibiting promising performances (e.g., leading to a new
architecture for traffic balancing in the cloud and edge clusters, with 60% and
400% gain in efficiency respectively and 1/3 of dedicated resources).
Description
Mención Internacional en el título de doctor
Keywords
Big data, Network slicing, Resource management, Network efficiency, Mobile networks, Slice orchestration, NFV, Network function virtualization