publication . Other literature type . Report . 2019

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

Khalili, Hazmeh; Papageorgiou; Siddiqui, Shuaib; Barrera, Julio; Huici, Felipe; Yasukata, Kenichi; Ciulli, Nicola; Cruschelli, Paolo; Kraja, Elian; Francesconi, Elio; ...
Open Access English
  • Published: 06 Feb 2019
  • Publisher: Zenodo
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.
Subjects
free text keywords: 5g, Orchestrator
Funded by
EC| 5GCITY
Project
5GCITY
5GCITY
  • Funder: European Commission (EC)
  • Project Code: 761508
  • Funding stream: H2020 | IA
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Zenodo
Other literature type . 2019
Provider: Datacite
Zenodo
Other literature type . 2019
Provider: Datacite
ZENODO
Report . 2019
Provider: ZENODO
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publication . Other literature type . Report . 2019

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

Khalili, Hazmeh; Papageorgiou; Siddiqui, Shuaib; Barrera, Julio; Huici, Felipe; Yasukata, Kenichi; Ciulli, Nicola; Cruschelli, Paolo; Kraja, Elian; Francesconi, Elio; ...