| [1] | GE Xiaohu, TU Song, MAO Guoqiang, et al. 5G ultra-dense cellular networks[J]. IEEE Wireless Communications, 2016, 23(1): 72–79. doi: 10.1109/mwc.2016.7422408 |
| [2] | SUGISONO K, FUKUOKA A, and YAMAZAKI H. Migration for VNF instances forming service chain[C]. The 7th IEEE International Conference on Cloud Networking, Tokyo, Japan, 2018: 1–3. doi: 10.1109/CloudNet.2018.8549194. |
| [3] | ZHENG Qinghua, LI Rui, LI Xiuqi, et al. Virtual machine consolidated placement based on multi-objective biogeography-based optimization[J]. Future Generation Computer Systems, 2016, 54: 95–122. doi: 10.1016/j.future.2015.02.010 |
| [4] | ZHANG Xiaoqing, YUE Qiang, and HE Zhongtang. Dynamic Energy-efficient Virtual Machine Placement Optimization for Virtualized Clouds[M]. JIA Limin, LIU Zhigang, QIN Yong, et al. Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume II. Berlin, Heidelberg: Springer, 2014, 288: 439–448. doi: 10.1007/978-3-642-53751-6_47. |
| [5] | ERAMO V, AMMAR M, and LAVACCA F G. Migration energy aware reconfigurations of virtual network function instances in NFV architectures[J]. IEEE Access, 2017, 5: 4927–4938. doi: 10.1109/ACCESS.2017.2685437 |
| [6] | ERAMO V, MIUCCI E, AMMAR M, et al. An approach for service function chain routing and virtual function network instance migration in network function virtualization architectures[J]. IEEE/ACM Transactions on Networking, 2017, 25(4): 2008–2025. doi: 10.1109/TNET.2017.2668470 |
| [7] | WEN Tao, YU Hongfang, SUN Gang, et al. Network function consolidation in service function chaining orchestration[C]. 2016 IEEE International Conference on Communications, Kuala Lumpur, Malaysia, 2016: 1–6. doi: 10.1109/ICC.2016.7510679. |
| [8] | 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 |
| [9] | CHENG Aolin, LI Jian, YU Yuling, et al. Delay-sensitive user scheduling and power control in heterogeneous networks[J]. IET Networks, 2015, 4(3): 175–184. doi: 10.1049/iet-net.2014.0026 |
| [10] | 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 |
| [11] | WANG Shangxing, LIU Hanpeng, GOMES P H, et al. Deep reinforcement learning for dynamic multichannel access in wireless networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2018, 4(2): 257–265. doi: 10.1109/TCCN.2018.2809722 |
| [12] | HUANG Xiaohong, YUAN Tingting, QIAO Guanghua, et al. Deep reinforcement learning for multimedia traffic control in software defined networking[J]. IEEE Network, 2018, 32(6): 35–41. doi: 10.1109/MNET.2018.1800097 |
| [13] | HE Ying, ZHANG Zheng, YU F R, et al. Deep-reinforcement-learning-based optimization for cache-enabled opportunistic interference alignment wireless networks[J]. IEEE Transactions on Vehicular Technology, 2017, 66(11): 10433–10445. doi: 10.1109/TVT.2017.2751641 |
| [14] | GLOROT X and BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C]. The International Conference on Artificial Intelligence and Statistics, Sardinia, 2010: 249–256. |
| [15] | PERUMAL V and SUBBIAH S. Power-conservative server consolidation based resource management in cloud[J]. International Journal of Network Management, 2014, 24(6): 415–432. doi: 10.1002/nem.1873 |
| [16] | QU Long, ASSI C, SHABAN K, et al. Delay-aware scheduling and resource optimization with network function virtualization[J]. IEEE Transactions on Communications, 2016, 64(9): 3746–3758. doi: 10.1109/TCOMM.2016.2580150 |