Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network

Lun TANG; Yannan WEI; Runlin MA; Xiaoyu HE; Qianbin CHEN

doi:10.11999/JEIT180771

Volume 41 Issue 7

Jul. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(7): 1533-1539

Lun TANG, Yannan WEI, Runlin MA, Xiaoyu HE, Qianbin CHEN. Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1533-1539. doi: 10.11999/JEIT180771

Citation:

Lun TANG, Yannan WEI, Runlin MA, Xiaoyu HE, Qianbin CHEN. Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1533-1539. doi: 10.11999/JEIT180771

Citation:

Lun TANG, Yannan WEI, Runlin MA, Xiaoyu HE, Qianbin CHEN. Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1533-1539. doi: 10.11999/JEIT180771

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Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network

doi: 10.11999/JEIT180771

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)

Received Date: 2018-08-03
Rev Recd Date: 2019-02-20

Available Online: 2019-03-19

Publish Date: 2019-07-01

Abstract

Abstract

To solve the problem of lacking efficient and dynamic resource allocation schemes for 5G Network Slicing (NS) in Cloud Radio Access Network (C-RAN) scenario in the existing researches, a virtual resource allocation algorithm for NS in virtualized C-RAN is proposed. Firstly, a stochastic optimization model in virtualized C-RAN network is established based on the Constrained Markov Decision Process (CMDP) theory, which maximizes the average sum rates of all slices as its objective, and is subject to the average delay constraint for each slice as well as the average network backhaul link bandwidth consumption constraint in the meantime. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states in the proposed CMDP optimization problem, the concept of Post-Decision State (PDS) as an " intermediate state” is introduced, which is used to describe the state of the system after the known dynamics, but before the unknown dynamics occur, and it incorporates all of the known information about the system state transition. Finally, an online learning based virtual resource allocation algorithm is presented for NS in virtualized C-RAN, where in each discrete resource scheduling slot, it will allocate appropriate Resource Blocks (RBs) and caching resource for each network slice according to the observed current system state. The simulation results reveal that the proposed algorithm can effectively satisfy the Quality of Service (QoS) demand of each individual network slice, reduce the pressure of backhaul link on bandwidth consumption and improve the system throughput.
- 5G Network Slicing (NS),
- Cloud Radio Access Network (C-RAN),
- Resource allocation,
- Markov Decision Process (MDP)

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References(11)

References

HOSSAIN E and HASAN M. 5G cellular: Key enabling technologies and research challenges[J]. IEEE Instrumentation & Measurement Magazine, 2015, 18(3): 11–21. doi: 10.1109/MIM.2015.7108393

CHECKO A, CHRISTIANSEN H L, YAN Ying, et al. Cloud RAN for mobile networks-A technology overview[J]. IEEE Communications Surveys & Tutorials, 2015, 17(1): 405–426. doi: 10.1109/COMST.2014.2355255

NIU Binglai, ZHOU Yong, SHAH-MANSOURI H, et al. A dynamic resource sharing mechanism for cloud radio access networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(12): 8325–8338. doi: 10.1109/TWC.2016.2613896

KALIL M, Al-DWEIK A, SHARKH M F A, et al. A framework for joint wireless network virtualization and cloud radio access networks for next generation wireless networks[J]. IEEE Access, 2017, 5: 20814–20827. doi: 10.1109/ACCESS.2017.2746666

BERTSEKAS D and GALLAGER R. Data Networks[M]. Englewood Cliffs: Prentice-Hall, 1991, 152–162.

YANG Jian, ZHANG Shuben, WU Xiaomin, et al. Online learning-based server provisioning for electricity cost reduction in data center[J]. IEEE Transactions on Control Systems Technology, 2017, 25(3): 1044–1051. doi: 10.1109/TCST.2016.2575801

KALIL M, SHAMI A, and YE Yinghua. Wireless resources virtualization in LTE systems[C]. Proceedings of 2014 IEEE Conference on Computer Communications Workshops, Toronto, Canada, 2014: 363–368. doi: 10.1109/INFCOMW.2014.6849259.

POWELL W B. Approximate Dynamic Programming: Solving the Curses of Dimensionality[M]. Hoboken, USA: Wiley, 2011, 289–388.

LAKSHMINARAYANAN C and BHATNAGAR S. Approximate dynamic programming with (min, +) linear function approximation for Markov decision processes[J]. arXiv preprint arXiv: 1403.4179, 2014.

LI Rongpeng, ZHAO Zhifeng, CHEN Xianfu, et al. TACT: A transfer actor-critic learning framework for energy saving in cellular radio access networks[J]. IEEE Transactions on Wireless Communications, 2014, 13(4): 2000–2011. doi: 10.1109/TWC.2014.022014.130840

HE Xiaoming, WANG Kun, HUANG Huawei, et al. Green resource allocation based on deep reinforcement learning in content-centric IoT[J]. IEEE Transactions on Emerging Topics in Computing, 2019. doi: 10.1109/TETC.2018.2805718

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