无线资源虚拟化映射方案多场景仿真性能比较
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摘要
为满足下一代移动通信技术多样化的网络优化需求,设计了不同优化目标的无线网络资源虚拟化映射方案,并通过仿真验证和对比分析各方案在不同网络负载水平下的性能表现。结果表明,追求网络总功率最小化、以节能为目标的优化方案适用于网络整体负载水平低,系统中有较多空闲基站的场景;追求用户总吞吐量、频谱利用率等同类目标最大化的面向无线资源的方案适用于网络处于中低水平负载的场景;追求用户满意度的面向用户方案对抗负载变化的能力较强,适用于重负载场景或负载水平快速波动的场景。映射方案的本质是物理资源在用户总吞吐量、网络总功率、用户满意度3个方面的相互置换,寻找各场景适合的优化目标,其实质是寻找各场景中转换效率最高的方案。
To meet the requirements of diversified network optimization for the next generation mobile communication technology, this paper provided resource virtualization mapping schemes of wireless network with different optimization objectives, and compared their performances under different network loads by simulation. The results show that the optimization scheme aiming at energy saving is suitable for the light load scenario for system withmany idle base stations.The wireless resource oriented scheme, which aims to maximize the total throughput and spectrum utilization of users, is suitable for the scenario where the network has medium or low loads.The user satisfaction-oriented scheme has a better robustness to load variation and it is suitable for heavy load scenarios.The essence of the mapping scheme is the mutual replacement of physical resources in aspects of total user throughput, total network power and user satisfaction to find the optimal target suitable for each scenario, which means finding the scheme with the highest conversion efficiency in each scenario.
To meet the requirements of diversified network optimization for the next generation mobile communication technology, this paper provided resource virtualization mapping schemes of wireless network with different optimization objectives, and compared their performances under different network loads by simulation. The results show that the optimization scheme aiming at energy saving is suitable for the light load scenario for system withmany idle base stations.The wireless resource oriented scheme, which aims to maximize the total throughput and spectrum utilization of users, is suitable for the scenario where the network has medium or low loads.The user satisfaction-oriented scheme has a better robustness to load variation and it is suitable for heavy load scenarios.The essence of the mapping scheme is the mutual replacement of physical resources in aspects of total user throughput, total network power and user satisfaction to find the optimal target suitable for each scenario, which means finding the scheme with the highest conversion efficiency in each scenario.
引文
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[6]姜鑫,杨龙祥,吴梦婷. 无线环境下的虚拟网络映射算法研究[J]. 计算机技术与发展,2017, 27(4):77-82.
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[8]MIRAHSAN M,SENARATH G,FARMANBAR H,et al. Admission control of wireless virtual networks in HetNets[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5):4 565-4 576.
[9]LIANG C,YU R,ZHANG X . Information-centric network function virtualization over 5G mobile wireless networks[J]. IEEE Network, 2015, 29(3):68-74.
[10]PARSAEEFARD S ,DAWADI R, DERAKHSHANI M,et al. Joint user-association and resource-allocation in virtualized wireless networks[J]. IEEE Access, 2016(4):2 738-2 750.
[11]HO T,TRAN N,KAZMI S,et al. Dynamic pricing for resource allocation in wireless network virtualization: a stackelberg game approach[C]//IEEE International Conference on Information Networking. Helsinki: IEEE.2017. DOI:10.1109/ICOIN.2017.7899529.
[12]ZHU K, HOSSAIN E.Virtualization of 5G cellular networks as a hierarchical combinatorial auction[J]. IEEE Transactions on Mobile Computing, 2016, 15(10):2 640-2 654.
[13]LIU G,YU F,JI H,et al.Virtual resource management in green cellular networks with shared full-duplex relaying and wireless virtualization:a game-based approach[J].IEEE Transactions on Vehicular Technology, 2016, 65(9):7 529-7 542.
[14]CABALLERO P ,BANCHS A ,VECIANA G,et al. Multi-tenant radio access network slicing: statistical multiplexing of apatial loads[J]. IEEE/ACM Transactions on Networking, 2017, 25(5):3 044-3 058.
[15]3GPP. TS 36.213: Physical Layer Procedures [EB/OL].[2018-10-30]. www.3gpp.org.
[2]RICHART M,BALIOSIAN J,SERRAT J,et al. Resource slicing in virtual wireless networks: a survey[J]. IEEE Transactions on Network and Service Management, 2016, 13(3):462-476.
[3]LIANG C,YU F . Wireless network virtualization: a survey, some research issues and challenges[J]. IEEE Communications Surveys & Tutorials. 1st quarter, 2015, 17(1):358-380.
[4]BELT J,AHMADI H,DOYLE L . A dynamic embedding algorithm for wireless network virtualization[C]// 80th Vehicular Technology Conference.Vancouver:IEEE.2014.DOI:10.1109/VTCFall.2014.6965811.
[5]曹傧,郎文强,李云. 基于最大收益的无线虚拟网络重映射算法[J]. 重庆邮电大学学报(自然科学版),2016. 28(5):620-627.
[6]姜鑫,杨龙祥,吴梦婷. 无线环境下的虚拟网络映射算法研究[J]. 计算机技术与发展,2017, 27(4):77-82.
[7]曹傧,夏士超,何芳,等. 无线网络虚拟化中的映射算法研究[J]. 通信学报,2017,38(1):35-43.
[8]MIRAHSAN M,SENARATH G,FARMANBAR H,et al. Admission control of wireless virtual networks in HetNets[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5):4 565-4 576.
[9]LIANG C,YU R,ZHANG X . Information-centric network function virtualization over 5G mobile wireless networks[J]. IEEE Network, 2015, 29(3):68-74.
[10]PARSAEEFARD S ,DAWADI R, DERAKHSHANI M,et al. Joint user-association and resource-allocation in virtualized wireless networks[J]. IEEE Access, 2016(4):2 738-2 750.
[11]HO T,TRAN N,KAZMI S,et al. Dynamic pricing for resource allocation in wireless network virtualization: a stackelberg game approach[C]//IEEE International Conference on Information Networking. Helsinki: IEEE.2017. DOI:10.1109/ICOIN.2017.7899529.
[12]ZHU K, HOSSAIN E.Virtualization of 5G cellular networks as a hierarchical combinatorial auction[J]. IEEE Transactions on Mobile Computing, 2016, 15(10):2 640-2 654.
[13]LIU G,YU F,JI H,et al.Virtual resource management in green cellular networks with shared full-duplex relaying and wireless virtualization:a game-based approach[J].IEEE Transactions on Vehicular Technology, 2016, 65(9):7 529-7 542.
[14]CABALLERO P ,BANCHS A ,VECIANA G,et al. Multi-tenant radio access network slicing: statistical multiplexing of apatial loads[J]. IEEE/ACM Transactions on Networking, 2017, 25(5):3 044-3 058.
[15]3GPP. TS 36.213: Physical Layer Procedures [EB/OL].[2018-10-30]. www.3gpp.org.