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Open AccessArticle

Reinforcement Learning Based Resource Management for Network Slicing

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123, Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
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Appl. Sci. 2019, 9(11), 2361; https://doi.org/10.3390/app9112361
Received: 3 May 2019 / Revised: 25 May 2019 / Accepted: 3 June 2019 / Published: 9 June 2019
Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users. View Full-Text
Keywords: network slicing; dynamic resource adjustment; Q-learning network slicing; dynamic resource adjustment; Q-learning
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Kim, Y.; Kim, S.; Lim, H. Reinforcement Learning Based Resource Management for Network Slicing. Appl. Sci. 2019, 9, 2361.

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