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TeraFlow: Secured Autonomic Traffic Management for a Tera of SDN flows

Authors: Ricard Vilalta; Raul Muñoz 0001; Ramon Casellas; Ricardo Martínez 0001; Víctor López 0001; Óscar González de Dios; Antonio Pastor 0001; +10 Authors

TeraFlow: Secured Autonomic Traffic Management for a Tera of SDN flows

Abstract

TeraFlow proposes a new type of secure, cloudnative Software Defined Networking (SDN) controller that will radically advance the state-of-the-art in beyond 5G networks by introducing novel micro-services architecture, and provide revolutionary features for both flow management (service layer) and optical/microwave network equipment integration (infrastructure layer) by adapting new data models. TeraFlow will also incorporate security using Machine Learning (ML) and forensic evidence for multi-tenancy based on Distributed Ledgers. Finally, this new SDN controller shall be able to integrate with the current Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) frameworks as well as to other networks. The target pool of TeraFlow stakeholders expands beyond the traditional telecom operators towards edge and hyperscale cloud providers.

Grant numbers : AURORAS - Autonomic and disaggregated optical networks leveraging edge computing and photonic technologies (RTI2018-099178-B-I00) project.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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