Abstract:In order to solve the problem of resource allocation between 5G virtual network slice, a resource scheduling mechanism based on Online Double Auction (ODA) is proposed. Firstly, the priority of network slices and unit resource quotes are determined according to different traffic needs and traffic benefits. Then, to maximize the network revenue, an offline single auction model is established. Further, based on the resources dynamic allocation and recycling, the price-updating algorithm is proposed to update the resource price in real time. Finally, the offline single auction mechanism and the price update mechanism are combined to establish ODA model and allocate resources dynamically for the network slices. The simulation results show that the proposed mechanism can improve network revenue and guarantee the QoS requirement of each slice user.
N ALLIANCE. NGMN 5G White Paper[OL]. https:// www.ngmn.org/fileadmin/ngmn/content/downloads/Technical/2015/NGMN_5G_White_Paper_V1_0.pdf, 2015
[2]
WAN NSFWA, ZHANG X, and NAKHAI M R. Sparse beamforming for real-time resource management and energy trading in Green C-RAN[J]. IEEE Transactions on Smart Grid, 2017, 8(4): 2022-2031. doi: 10.1109/TSG.2016. 2601718.
[3]
CHEN J L, MA Y W, KUO H Y, et al. Software-defined network virtualization platform for enterprise network resource management[J]. IEEE Transactions on Emerging Topics in Computing, 2016, 4(2): 179-186. doi: 10.1109/ TETC.2015.2478757.
[4]
PINGZHI F, JING Z, and CHIH-LIN I. 5G high mobility wireless communications: challenges and solutions[J]. China Communications, 2017, 13(Suppl.2): 1-13. doi: 10.1109/CC.2016.7833456.
[5]
WANG X, CHE H, LI K, et al. An intelligent economic approach for dynamic resource allocation in cloud services[J]. IEEE Transactions on Cloud Computing, 2015, 3(3): 275-289. doi: 10.1109/TCC.2015.2415776.
[6]
LOLOS K, KONSTANTINOU I, KANTERE V, et al. Adaptive state space partitioning of markov decision processes for elastic resource management[C]. 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, USA, 2017: 191-194. doi: 10.1109/ICDE.2017.72.
[7]
YAN Y, SI P, and ZHANG Y. B-CaB: Optimizing the SP,s bidding for cache and band resources in virtualized wireless networks[C]. International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, China, 2016: 17-22. doi: 10.1109/ICNISC.2016.014.
[8]
MASHAYEKHY L, NEJAD M M, and GROSU D. Physical machine resource management in clouds: A mechanism design approach[J]. IEEE Transactions on Cloud Computing, 2015, 3(3): 247-260. doi: 10.1109/TCC.2014.2369419.
[9]
SEMASINGHE P, MAGHSUDI S, and HOSSAIN E. Game theoretic mechanisms for resource management in massive wireless IoT systems[J]. IEEE Communications Magazine, 2017, 55(2): 121-127. doi: 10.1109/MCOM.2017.1600568CM.
[10]
KAMEL M I, LONG B L, et al. LTE wireless network virtualization: dynamic slicing via flexible scheduling[C]. IEEE Vehicular Technology Conference, Vancouver, Canada. 2014: 1-5. doi: 10.1109/VTCFall.2014.6966044.
[11]
SCIANCALEPORE V, SAMDANIS K, COSTA-PEREZ X, et al. Mobile traffic forecasting for maximizing 5G network slicing resource utilization[C]. IEEE Conference on Computer Communications, Atlanta, USA, 2017: 1-9. doi: 10.1109/INF OCOM.2017.8057230.
[12]
ZHU K and HOSSAIN E. Virtualization of 5G cellular networks as a hierarchical combinatorial auction[J]. IEEE Transactions on Mobile Computing, 2016, 15(10): 2640-2654. doi: 10.1109/TMC.2015.2506578.