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Orchestrator design, service programming and machine learning models ((D4.1)

Authors: Khalili, Hazmeh; Papageorgiou; Siddiqui, Shuaib; Barrera, Julio; Huici, Felipe; Yasukata, Kenichi; Ciulli, Nicola; +10 Authors

Orchestrator design, service programming and machine learning models ((D4.1)

Abstract

This document describes the components of the 5GCity architecture related to orchestration, service programming, and machine learning as main outcomes of tasks T4.1, T4.2, and T4.3. The overall 5GCity architecture is described in Deliverable D2.2 [1] and based on pilot requirements introduced in Deliverable D2.1 [2]. Our orchestration, service programming, and machine learning components are vital for addressing challenges of state-of-the-art 5G orchestrators and platforms, such as multi-tenancy support and efficient configuration and resource placement.

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Orchestrator, 5g

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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