LINK@KOREATECH
Intelligent Network

Intelligent Networking (5G/6G)

  1. CJ. Bernardos, A. Rahman, JC. Zuniga, LM. Contreras, P. Aranda, P. Lynch, "Network Virtualization Research Challenges," IETF RFC 8568, April, 2019.
  1. H. Cao, S. Wu, Y. Hu, Y. Liu and L. Yang, "A survey of embedding algorithm for virtual network embedding," in China Communications, 2019.
  2. R. Boutaba et. al., "A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities," Journal of Internet Services and Applications, 2018.
    • Refer to pages 50 to 52 in section 7.1
  3. H. Cao, H. Hu, Z. Qu and L. Yang, "Heuristic solutions of virtual network embedding: A survey," in China Communications, 2018.
  4. A. Fischer et. al., "Virtual Network Embedding: A Survey," IEEE Communications Surveys & Tutorials, 2013.
  1. Zhang, Peiying, et al. "Security aware virtual network embedding algorithm using information entropy TOPSIS." Journal of Network and Systems Management 28.1, 2020.
  2. S. Karmoshi, A. Hawbani, A. Ghannami, S. Mohammed and M. Zhu, "VNE-Greedy: Virtual Network Embedding Algorithm Based on OpenStack Cloud Computing Platform," 2016 6th International Conference on Digital Home (ICDH), 2016.
  3. [GRC] L. Gong, Y. Wen, Z. Zhu, and T. Lee, "Toward Profit-seeking Virtual Network Embedding Algorithm via Global Resource Capacity," IEEE INFOCOM, pp. 1–9, Apr. 2014.
  4. [NodeRank] M. Feng, L. Zhang, X. Zhu, J. Wang, Q. Qi and J. Liao, "Topology-aware virtual network embedding through the degree," National Doctoral Academic Forum on Information and Communications Technology, 2013.
  5. H. Kim and S. Lee, "Greedy virtual network embedding under an exponential cost function," The International Conference on Information Network 2012, 2012.
  6. X. Cheng, et. al., "Virtual Network Embedding Through Topology-Aware Node Ranking," ACM SIGCOMM Computer Communication Review, 2011.
  7. M. Yu, Y. Yi, J. Rexford, and M. Chiang, “Rethinking Virtual Network Embedding: Substrate Support for Path Splitting and Migration,” ACM SIGCOMM Computer Communication Review, 2008.
  1. N. Shahriar et al., “Virtual Network Survivability through Joint Spare Capacity Allocation and Embedding,” IEEE Journal on Selected Areas in Communications, 2018.
  2. [D-ViNE] [R-ViNE] M. Chowdhury, M. R. Rahman and R. Boutaba, "ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping," IEEE/ACM Transactions on Networking, 2012
  1. Khoa T. D. Nguyen, Changcheng Huang, "Distributed Parallel Genetic Algorithm for Online Virtual Network Embedding," International Journal of Communication Systems, 2020
  2. P. Zhang, Y. Hong, X. Pang and C. Jiang, "VNE-HPSO: Virtual Network Embedding Algorithm Based on Hybrid Particle Swarm Optimization," IEEE Access, 2020
  3. L. Boyang, W. Muqing and Z. Haosen, "Virtual Network Embedding Based on Hybrid Adaptive Genetic Algorithm," IEEE 5th International Conference on Computer and Communications (ICCC), 2019.
      [2021]
  1. [GCN-DRL-VNE] P. Zhang, et al., "Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning," in IEEE Internet of Things Journal, 2021.

  2. [QS-DRL-VNE] Wang, Chao, et al. "VNE Solution for Network Differentiated QoS and Security Requirements: from The Perspective of Deep Reinforcement Learning," Computing, 2021.

    3.   [2020]
  3. [GNN-VNE] A. Rkhami, et.al., "On the Use of Graph Neural Networks for Virtual Network Embedding," 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020.
  4. [TS-DRL-VNE] [FAM-DRL-VNE] [MPT-DRL-VNE] Zhang, Shidong, et al. "Network Resource Allocation Strategy Based on Deep Reinforcement Learning," IEEE Open Journal of the Computer Society 1, 2020.
  5. [PN-VNE] Wang, Cong, et al. "Modeling on Virtual Network Embedding Using Reinforcement Learning," Concurrency and Computation: Practice and Experience, 2020.
  6. [CDRL] H. Yao, S. Ma, J. Wang, P. Zhang, C. Jiang and S. Guo, "A Continuous-Decision Virtual Network Embedding Scheme Relying on Reinforcement Learning," in IEEE Transactions on Network and Service Management, 2020.
  7. [PP-RL-VNE] D. Andreoletti, T. Velichkova, G. Verticale, M. Tornatore and S. Giordano, "A Privacy-Preserving Reinforcement Learning Algorithm for Multi-Domain Virtual Network Embedding," in IEEE Transactions on Network and Service Management, 2020.
  8. [WSN-QL-VNE] Afifi, Haitham, and Holger Karl. "Reinforcement Learning for Virtual Network Embedding in Wireless Sensor Networks." 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2020.
  9. [QL-VNE] Y. Yuan, Z. Tian, C. Wang, F. Zheng, and Y. Lv, “A Q-learning-based Approach for Virtual Network Embedding in Data Center,” Neural Computing and Applications, 2020.
  10. [EAMCM] P. T. Anh Quang, Y. Hadjadj-Aoul and A. Outtagarts, "Evolutionary Actor-Multi-Critic Model for VNF-FG Embedding," 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020.
  11. [A3C-GCN] Z. Yan, J. Ge, Y. Wu, L. Li and T. Li, "Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks," in IEEE Journal on Selected Areas in Communications, 2020.

    12.   [2019]
  12. [NVFdeep] Y. Xiao, Q. Zhang, F. Liu, J. Wang, M. Zhao, Z. Zhang, and J. Zhang, "NFVdeep: Adaptive Online Service Function Chain Deployment with Deep Reinforcement Learning," ACM Proceedings of the International Symposium on Quality of Service (IWQoS), pages 1–21, NY, USA, 2019.
  13. [Data-driven] H. Wang, Y. Wu, G. Min, J. Xu, and P. Tang, “Data-driven Dynamic Resource Scheduling for Network Slicing: A Deep Reinforcement Learning Approach,” Information Science, vol. 498, pp. 106–116, Sep. 2019.
  14. [DeepViNE] M. Dolati, S. B. Hassanpour, M. Ghaderi, and A. Khonsari, "DeepViNE: Virtual Network Embedding with Deep Reinforcement Learning," IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 879–885, IEEE, 2019.
  15. [VNE-TD] S. Wang, J. Bi, J. Wu, A. V. Vasilakos, and Q. Fan, "VNE-TD: A Virtual Network Embedding Algorithm Based on Temporal-Difference Learning,” Computer Networks, vol. 161, pp. 251–263, Oct. 2019.

    16.   [2018]
  16. [RDAM] H. Yao, B. Zhang, P. Zhang, S Wu, C. Jiang, S. Guo, "RDAM: A Reinforcement Learning Based Dynamic Attribute Matrix Representation for Virtual Network Embedding," IEEE Transactions on Emerging Topics in Computing, 2018.
  17. [Q-CD-VNE] M. He, L. Zhuang, S. Tian, G. Wang, K. Zhang , "Multi-objective virtual network embedding algorithm based on Q-learning and curiosity-driven," EURASIP Journal on Wireless Communications and Networking, 2018.
  18. [CNN-VNE] H. Yao, X. Chen, M. Li, P. Zhang, L. Wang, "A Novel Reinforcement Learning Algorithm for Virtual Network Embedding," Neurocomputing, 2018.
  19. [Z-TORCH] V. Sciancalepore, F. Z. Yousaf and X. Costa-Perez, "Z-TORCH: An Automated NFV Orchestration and Monitoring Solution," IEEE Transactions on Network and Service Management, 2018

    20.   [2014]
  20. [Extended-QL-VNE] R. Mijumbi, J.-L. Gorricho, J. Serrat, M. Claeys, F. D. Turck, and S. Latre, "Design and Evaluation of Learning Algorithms for Dynamic Resource Management in Virtual Networks," IEEE Network Operations and Management Symposium (NOMS), pages 1-9, 2014.
  21. [DQN-VNE] R. Mijumbi, J.-L. Gorricho, J. Serrat, M. Claeys, J. Famaey, and F. D. Turck. "Neural Network-based Autonomous Allocation of Resources in Virtual Networks," IEEE European Conference on Networks and Communications (EuCNC), pages 1-6, 2014.
      [2021]
  1. Liu, Yongshuai, Jiaxin Ding and Xin Liu. “Resource Allocation Method for Network Slicing Using Constrained Reinforcement Learning.” 2021 IFIP Networking Conference, 2021.
  2. A. Rkhami, Y. Hadjadj-Aoul and A. Outtagarts, "Learn to improve: A novel deep reinforcement learning approach for beyond 5G network slicing," 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), 2021.
  3. Z. Mlika and S. Cherkaoui, "Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles," IEEE Network, 2021.
  4. Villota Jácome, et al., "Admission Control for 5G Network Slicing based on (Deep) Reinforcement Learning," TechRxiv, 2021.
  5. B. Sihem, B. Bouziane, K. Adlen, "On using reinforcement learning for network slice admission control in 5G: Offline vs. online," International Journal of Communication Systems, 2021.
  6. Y. Kim and H. Lim, "Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing," in IEEE Access, 2021.
  7. Y. Shao, R. Li, Z. Zhao and H. Zhang, "Graph Attention Network-based DRL for Network Slicing Management in Dense Cellular Networks," 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021.
  8. Liu, Qiang, Nakjung Choi and Tao Han. “OnSlicing: online end-to-end network slicing with reinforcement learning.” Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, 2021.

    9.   [2020]
  9. Liu, Qiang, Tao Han, Ning Zhang and Ye Wang. “DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing.” GLOBECOM, 2020.
  10. Liu, Qiang, Tao Han and Ephraim Moges. “EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning.” 2020 IEEE 40th International Conference on Distributed Computing Systems, 2020.
  11. L. Zhao and L. Li, "Reinforcement Learning for Resource Mapping in 5G Network Slicing," 2020 5th International Conference on Computer and Communication Systems (ICCCS), 2020.
  12. Y. Liu, J. Ding and X. Liu, "A Constrained Reinforcement Learning Based Approach for Network Slicing," 2020 IEEE 28th International Conference on Network Protocols (ICNP), 2020.

    13.   [2019]
  13. Y. Liu, J. Ding and X. Liu, "A Constrained Reinforcement Learning Based Approach for Network Slicing," 2020 IEEE 28th International Conference on Network Protocols (ICNP), 2020.
  14. J. Koo, V. B. Mendiratta, M. R. Rahman and A. Walid, "Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics," 2019 15th International Conference on Network and Service Management (CNSM), 2019.
  15. V. Sciancalepore, X. Costa-Perez and A. Banchs, "RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker," in IEEE/ACM Transactions on Networking, 2019.
  16. Haozhe Wang, Yulei Wu, Geyong Min, Jie Xu, Pengcheng Tang, "Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach," Information Sciences, 2019.
  17. Y. Kim, S. Kim, H. Lim, "Reinforcement Learning Based Resource Management for Network Slicing," Applied Sciences, 2019.
  18. S. de Bast, R. Torrea-Duran, A. Chiumento, S. Pollin and H. Gacanin, "Deep Reinforcement Learning for Dynamic Network Slicing in IEEE 802.11 Networks," IEEE INFOCOM 2019.

    19.   [2018]
  19. R. Li et al., "Deep Reinforcement Learning for Resource Management in Network Slicing," in IEEE Access, 2018.

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