This paper deals with the problem of resource management in Multi-Access Networks. A Reinforcement Learning based hierarchical control strategy is presented. The main contribution of the proposed approach is its capability of simultaneously tacking the load balancing and QoS management problems in a scalable, dynamic and closed-loop way. The effectiveness of the proposed solution has been proved in a specific case study in the context of which the performances of the proposed algorithm have been compared with a standard load balancing controller.
Hierarchical RL for load balancing and QoS management in multi-access networks / Ornatelli, A.; Tortorelli, A.; Giuseppi, A.; Priscoli, F. D.. - (2021), pp. 886-891. (Intervento presentato al convegno 29th Mediterranean Conference on Control and Automation, MED 2021 tenutosi a Bari; Italy) [10.1109/MED51440.2021.9480246].
Hierarchical RL for load balancing and QoS management in multi-access networks
Ornatelli A.
;Tortorelli A.;Giuseppi A.;Priscoli F. D.
2021
Abstract
This paper deals with the problem of resource management in Multi-Access Networks. A Reinforcement Learning based hierarchical control strategy is presented. The main contribution of the proposed approach is its capability of simultaneously tacking the load balancing and QoS management problems in a scalable, dynamic and closed-loop way. The effectiveness of the proposed solution has been proved in a specific case study in the context of which the performances of the proposed algorithm have been compared with a standard load balancing controller.File | Dimensione | Formato | |
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Note: DOI: 10.1109/MED51440.2021.9480246
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