The Network Slicing (NS) paradigm enables the partition of physical and virtual resources among multiple logical networks, possibly managed by different tenants. In such a scenario, network resources need to be dynamically allocated according to the slice requirements. In this paper, we attack the above problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agent training is carried out following the Advantage Actor Critic algorithm, which permits to handle continuous action spaces. By means of extensive simulations, we show that our approach yields better performance than both a static allocation of system resources and an efficient empirical strategy. At the same time, the proposed system ensures high adaptability to different scenarios without the need for additional training.
Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario
Mason F.;Zanella A.
2022
Abstract
The Network Slicing (NS) paradigm enables the partition of physical and virtual resources among multiple logical networks, possibly managed by different tenants. In such a scenario, network resources need to be dynamically allocated according to the slice requirements. In this paper, we attack the above problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agent training is carried out following the Advantage Actor Critic algorithm, which permits to handle continuous action spaces. By means of extensive simulations, we show that our approach yields better performance than both a static allocation of system resources and an efficient empirical strategy. At the same time, the proposed system ensures high adaptability to different scenarios without the need for additional training.File | Dimensione | Formato | |
---|---|---|---|
Using_Distributed_Reinforcement_Learning_for_Resource_Orchestration_in_a_Network_Slicing_Scenario.pdf
non disponibili
Tipologia:
Published (publisher's version)
Licenza:
Accesso privato - non pubblico
Dimensione
2.37 MB
Formato
Adobe PDF
|
2.37 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
2105.07946.pdf
accesso aperto
Tipologia:
Preprint (submitted version)
Licenza:
Accesso libero
Dimensione
2.35 MB
Formato
Adobe PDF
|
2.35 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.