Publication: DeepCog: cognitive network management in sliced 5G Networks with deep learning
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2019-06-17
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IEEE
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Abstract
Network slicing is a new paradigm for future 5G
networks where the network infrastructure is divided into slices
devoted to different services and customized to their needs.
With this paradigm, it is essential to allocate to each slice the
needed resources, which requires the ability to forecast their
respective demands. To this end, we present DeepCog, a novel
data analytics tool for the cognitive management of resources
in 5G systems. DeepCog forecasts the capacity needed to accommodate
future traffic demands within individual network
slices while accounting for the operator’s desired balance between
resource overprovisioning (i.e., allocating resources exceeding
the demand) and service request violations (i.e., allocating less
resources than required). To achieve its objective, DeepCog hinges
on a deep learning architecture that is explicitly designed for
capacity forecasting. Comparative evaluations with real-world
measurement data prove that DeepCog’s tight integration of
machine learning into resource orchestration allows for substantial
(50% or above) reduction of operating expenses with
respect to resource allocation solutions based on state-of-theart
mobile traffic predictors. Moreover, we leverage DeepCog
to carry out an extensive first analysis of the trade-off between
capacity overdimensioning and unserviced demands in adaptive,
sliced networks and in presence of real-world traffic.
Description
Proceeding of: 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019), Paris (France), 29 April - 2 May, 2019.
Keywords
Deep learning, DeepCog, Mobile traffic predictors, Sliced 5G networks
Bibliographic citation
IEEE INFOCOM 2019- IEEE Conference on Computer Communications, 29 April-2 May 2019, Paris, France [proceedings], 9 pp.