Demonstrating fluid connectivity for Computing Anywhere with 6G Cloud Networks

Demonstrating fluid connectivity for Computing Anywhere with 6G Cloud Networks

Dimitrovski, T.
Chiscop, I.
Pileggi, P.
Panneman, J.

2022

Anticipating a highly customized and flexible virtual service portfolio of 6G networks, unprecedented complexity will challenge network manageability if left to human operators. Automation and autonomy are key enablers of next generation cloud networks. With this in mind, we present a demonstrator of ML-assisted user plane resource management and orchestration. We considered a static resource management policy and implemented an intelligent agent that manages a virtual router location with the goal to minimize the latency of mobile users in a distributed network. The demonstrator illustrates a bottom-up way forward to develop an artificial network engineer that will improve the manageability of next generation cloud networking starting from small scale within network partitions, but effectively creating a fluid wide-area connectivity for computing anywhere with 6G cloud networks. We identify key open questions that need to be answered in order for the solution to scale to the incoming 6G cloud networks. (C) 2022 IEEE.

6G Cloud Network
Cloud resource management and orchestration
Network Function Management
Reinforcement Learning
Natural resources management
Next generation networks
Resource allocation
Virtual reality
Wide area networks
6g cloud network
Cloud networks
Cloud resource management and orchestration
Human operator
Network function management
Network functions
Reinforcement learnings
Resource management
Service portfolio
Virtual service
Reinforcement learning

http://resolver.tudelft.nl/uuid:b49731e5-94be-47ff-a5d5-c855526806c6

982030

Institute of Electrical and Electronics Engineers Inc.

9781665459754

2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, 61-66

conference paper