LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude o...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2021-01, Vol.20 (1), p.667-682
Main Authors: Zanzi, Lanfranco, Sciancalepore, Vincenzo, Garcia-Saavedra, Andres, Schotten, Hans Dieter, Costa-Perez, Xavier
Format: Article
Language:English
Subjects:
5G mobile communication
5G-and-beyond
Aggregates
Computing time
Decisions
Electronic mail
latency control
MAB
Markov processes
Network latency
Network slicing
Resource management
Robustness (mathematics)
Throughput
virtualization
Wireless communication
Wireless networks
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Summary:Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.3027963