AI-Based Resource Management in beyond 5G Cloud Native Environment

Abderrahmane Boudi, Miloud Bagaa, Petteri Pöyhönen, Tarik Taleb, Hannu Flinck

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

5G system and beyond targets a large number of emerging applications and services that will create extra overhead on network traffic. These industrial verticals have aggressive, contentious, and conflicting requirements that make the network have an arduous mission for achieving the desired objectives. It is expected to get requirements with close to zero time latency, high data rate, and network reliability. Fortunately, a ray of hope comes shining the way of telecom providers with the new progress and achievements in machine learning, cloud computing, micro-services, and the ETSI ZSM era. For this reason there is a colossal impetus from industry and academia toward applying these techniques by creating a new concept called CCN environment that can cohabit and adapt according to the network and resource state, and perceived KPIs. In this article, we pursue the aforementioned concept by providing a unified hierarchical closed-loop network and service management framework that can meet the desired objectives. We propose a cloud-na-tive simulator that accurately mimics cloud-native environments, and enables us to quickly evaluate new frameworks and ideas. The simulation results demonstrate the efficiency of our simulator for parroting the real testbeds in various metrics.

Original languageEnglish
Article number9373016
Pages (from-to)128-135
Number of pages8
JournalIEEE NETWORK
Volume35
Issue number2
DOIs
Publication statusPublished - Mar 2021
MoE publication typeA1 Journal article-refereed

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