[1] |
兰巨龙, 程东年, 胡宇翔 . 可重构信息通信基础网络体系研究[J]. 通信学报, 2014,35(1): 128-139.
|
|
LAN J L , CHENG D N , HU Y X . Research on reconfigurable information communication basal network architecture[J]. Journal on Communications, 2014,35(1): 128-139.
|
[2] |
胡宇翔, 伊鹏, 孙鹏浩 ,等. 全维可定义的多模态智慧网络体系研究[J]. 通信学报, 2019,40(8): 1-12.
|
|
HU Y X , YI P , SUN P H ,et al. Research on the full-dimensional defined polymorphic smart network[J]. Journal on Communications, 2019,40(8): 1-12.
|
[3] |
NGMN Alliance. Description of network slicing concept[EB].(2017-09-11)[2022-01-04]
|
[4] |
AFOLABI I , BAGAA M , TALEB T ,et al. End-to-end network slicing enabled through network function virtualization[C]// Proceedings of 2017 IEEE Conference on Standards for Communications and Networking. Piscataway:IEEE Press, 2017: 30-35.
|
[5] |
ZAMAN Z , RAHMAN S , NAZNIN M . Novel approaches for VNF requirement prediction using DNN and LSTM[C]// 2019 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2019: 1-6.
|
[6] |
QIU F , ZHANG B , GUO J . A deep learning approach for VM workload prediction in the cloud[C]// Proceedings of 2016 17th IEEE/ACIS International Conference on Software Engineering,Artificial Intelligence,Networking and Parallel/Distributed Computing (SNPD). Piscataway:IEEE Press, 2016: 319-324.
|
[7] |
唐伦, 周钰, 杨友超 ,等. 5G网络切片场景中基于预测的虚拟网络功能动态部署算法[J]. 电子与信息学报, 2019,41(9): 2071-2078.
|
|
TANG L , ZHOU Y , YANG Y C ,et al. Virtual network function dynamic deployment algorithm based on prediction for 5G network slicing[J]. Journal of Electronics & Information Technology, 2019,41(9): 2071-2078.
|
[8] |
TANG H , ZHOU D , CHEN D . Dynamic network function instance scaling based on traffic forecasting and VNF placement in operator data centers[J]. IEEE Transactions on Parallel and Distributed Systems, 2019,30(3): 530-543.
|
[9] |
ALAWE I , HADJADJ-AOUL Y KSENTINI A ,et al. Smart scaling of the 5G core network:an RNN-based approach[C]// Proceedings of 2018 IEEE Global Communications Conference. Piscataway:IEEE Press, 2018: 1-6.
|
[10] |
THAKKAR H K , DEHURY C K , SAHOO P K . MUVINE:multi-stage virtual network embedding in cloud data centers using reinforcement learning-based predictions[J]. IEEE Journal on Selected Areas in Communications, 2020,38(6): 1058-1074.
|
[11] |
PATEL Y S , VERMA D , MISRA R . Deep learning based resource allocation for auto-scaling VNFs[C]// Proceedings of 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems. Piscataway:IEEE Press, 2019: 1-6.
|
[12] |
SCALINGI A , ESPOSITO F , MUHAMMAD W ,et al. Scalable provisioning of virtual network functions via supervised learning[C]// Proceedings of 2019 IEEE Conference on Network Softwarization (NetSoft). Piscataway:IEEE Press, 2019: 423-431.
|
[13] |
TONG R M , XU S Y , HU B ,et al. VNF dynamic scaling and deployment algorithm based on traffic prediction[C]// Proceedings of 2020 International Wireless Communications and Mobile Computing (IWCMC). Piscataway:IEEE Press, 2020: 789-794.
|
[14] |
TAO J , LU Z , CHEN Y ,et al. Adaptive VNF scaling approach with proactive traffic prediction in NFV-enabled clouds[C]// ACM Turing Award Celebration Conference. New York:ACM Press, 2021: 166-172.
|
[15] |
MIJUMBI R , HASIJA S , DAVY S ,et al. Topology-aware prediction of virtual network function resource requirements[J]. IEEE Transactions on Network and Service Management, 2017,14(1): 106-120.
|
[16] |
JALODIA N , HENNA S , DAVY A . Deep reinforcement learning for topology-aware VNF resource prediction in NFV environments[C]// Proceedings of 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks. Piscataway:IEEE Press, 2019: 1-5.
|
[17] |
KIM H G , JEONG S Y , LEE D Y ,et al. A deep learning approach to VNF resource prediction using correlation between VNFs[C]// Proceedings of 2019 IEEE Conference on Network Softwarization (NetSoft). Piscataway:IEEE Press, 2019: 444-449.
|
[18] |
ZHUANG Z R , WANG J Y , QI Q ,et al. Toward greater intelligence in route planning:a graph-aware deep learning approach[J]. IEEE Systems Journal, 2020,14(2): 1658-1669.
|
[19] |
SHEN Y F , SHI Y M , ZHANG J ,et al. Graph neural networks for scalable radio resource management:architecture design and theoretical analysis[J]. IEEE Journal on Selected Areas in Communications, 2021,39(1): 101-115.
|
[20] |
FOUKAS X , PATOUNAS G , ELMOKASHFI A ,et al. Network slicing in 5G:survey and challenges[J]. IEEE Communications Magazine, 2017,55(5): 94-100.
|
[21] |
THOMAS R W , FRIEND D H , DASILVA L A ,et al. Cognitive networks:adaptation and learning to achieve end-to-end performance objectives[J]. IEEE Communications Magazine, 2006,44(12): 51-57.
|
[22] |
KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks[J]. arXiv Preprint,arXiv:1609.02907, 2016.
|
[23] |
UHLIG S , QUOITIN B , LEPROPRE J ,et al. Providing public intradomain traffic matrices to the research community[J]. ACM SIG COMM Computer Communication Review, 2006,36(1): 83-86.
|
[24] |
武静雯, 江凌云, 刘祥军 . 基于特征选择的 VNF 资源需求预测方法[J]. 计算机应用研究, 2021,38(10): 3131-3136,3142.
|
|
WU J W , JIANG L Y , LIU X J . VNF resource demand forecast method based on feature selection[J]. Application Research of Computers, 2021,38(10): 3131-3136,3142.
|
[25] |
WANG B , LUO X Y , ZHANG F B ,et al. Graph-based deep modeling and real time forecasting of sparse spatio-temporal data[J]. arXiv Preprint,arXiv:1804.00684, 2018.
|