TY - GEN
T1 - A Hard and Soft Hybrid Slicing Framework for Service Level Agreement Guarantee via Deep Reinforcement Learning
AU - Zhang, Heng
AU - Pan, Guangjin
AU - Xu, Shugong
AU - Zhang, Shunqing
AU - Jiang, Zhiyuan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utmzed for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices' SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices' SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum efficiency after convergence.
AB - Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utmzed for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices' SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices' SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum efficiency after convergence.
KW - deep reinforcement learning
KW - Network slicing
KW - resource allocation
KW - service level agreements
UR - http://www.scopus.com/inward/record.url?scp=85137752723&partnerID=8YFLogxK
U2 - 10.1109/VTC2022-Spring54318.2022.9860789
DO - 10.1109/VTC2022-Spring54318.2022.9860789
M3 - Conference Proceeding
AN - SCOPUS:85137752723
T3 - IEEE Vehicular Technology Conference
BT - 2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring
Y2 - 19 June 2022 through 22 June 2022
ER -