Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario
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arXiv
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| Format: | Preprint |
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2021
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| _version_ | 1866914918050037760 |
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| author | Mason, Federico Nencioni, Gianfranco Zanella, Andrea |
| author_facet | Mason, Federico Nencioni, Gianfranco Zanella, Andrea |
| contents | 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 slices' 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 agents' training is carried out following the Advantage Actor Critic algorithm, which allows 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2105_07946 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario Mason, Federico Nencioni, Gianfranco Zanella, Andrea Multiagent Systems 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 slices' 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 agents' training is carried out following the Advantage Actor Critic algorithm, which allows 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. |
| title | Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2105.07946 |