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Fifth Generation (5G) networks are envisioned to be fully autonomous inaccordance to the ETSI-defined Zero touch network and Service Management (ZSM)concept. To this end, purpose-specific Machine Learning (ML) models can be usedto manage and control physical as well as virtual network resources in a waythat is fully compliant to slice Service Level Agreements (SLAs), while alsoboosting the revenue of the underlying physical network operator(s). This isbecause specially designed and trained ML models can be both proactive and veryeffective against slice management issues that can induce significant SLApenalties or runtime costs. However, reaching that point is very challenging.5G networks will be highly dynamic and complex, offering a large scale ofheterogeneous, sophisticated and resource-demanding 5G services as networkslices. This raises a need for a well-defined, generic and step-wise roadmap todesigning, building and deploying efficient ML models as collaborativecomponents of what can be defined as Cognitive Network and Slice Management(CNSM) 5G systems. To address this need, we take a use case-driven approach todesign and present a novel Integrated Methodology for CNSM in virtualized 5Gnetworks based on a concrete eHealth use case, and elaborate on it to derive ageneric approach for 5G slice management use cases. The three fundamentalcomponents that comprise our proposed methodology include (i) a 5G CognitiveWorkflow model that conditions everything from the design up to the finaldeployment of ML models; (ii) a Four-stage approach to Cognitive SliceManagement with an emphasis on anomaly detection; and (iii) a Proactive ControlScheme for the collaboration of different ML models targeting different slicelife-cycle management problems.

Integrated Methodology to Cognitive Network Slice Management in Virtualized 5G Networks