Weitere Kapitel dieses Buchs durch Wischen aufrufen
Resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. In this chapter, we propose a reinforcement learning based dynamic attribute matrix representation (RDAM) algorithm for virtual network embedding. The RDAM algorithm decomposes the process of node mapping into the following three steps: (1) static representation of substrate physical network. (2) dynamic update of substrate physical network. (3) Reinforcement-Learning-Based algorithm. Then, we design and implement a policy network based on reinforcement learning to make node mapping decisions. We use policy gradient to achieve optimization automatically by training the policy network with the historical data based on virtual network requests.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
J. L. Chen, Y. W. Ma, H. Y. Kuo, and C. S. Yang, “Software-defined network virtualization platform for enterprise network resource management,” IEEE Transactions on Emerging Topics in Computing, vol. 4, no. 2, pp. 179–186, 2016. CrossRef
J. Lu and J. Turner, “Efficient mapping of virtual networks onto a shared substrate,” Washington University in St Louis, 2006.
Z. Zhen, T. Jiang, W. Zhang, H. Yao, and S. Xiao, “Analyzing speech of patients with vocal polyps based on channel parameters and fuzzy logic systems,” Computers & Mathematics with Applications, vol. 62, no. 7, pp. 2834–2842, 2011. CrossRef
D. G. Andersen, “Theoretical approaches to node assignment,” Computer Science Department, 2002.
Zhao, Chenglin, Haipeng, Zhou, and Zheng, “Optimization of multiband spectrum sensing for cognitive radio networks under sensing capability constrains,” China Communications, vol. 7, no. 5, pp. 129–136, 2010.
C. Jiang, C. Yan, K. J. R. Liu, and R. Yong, “Network economics in cognitive networks,” IEEE Communications Magazine, vol. 53, no. 5, pp. 75–81, 2015. CrossRef
H. Li, M. Dong, K. Ota, and M. Guo, “Pricing and repurchasing for big data processing in multi-clouds,” IEEE Transactions on Emerging Topics in Computing, vol. 4, no. 2, pp. 266–277, 2016. CrossRef
X. Cheng, S. Su, Z. Zhang, H. Wang, F. Yang, Y. Luo, and J. Wang, “Virtual network embedding through topology-aware node ranking,” Acm Sigcomm Computer Communication Review, vol. 41, no. 2, pp. 38–47, 2011. CrossRef
Z. Tong, C. Yan, C. Jiang, and K. J. R. Liu, “Pricing game for time mute in femto-macro co-existent networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 4, pp. 2118–2130, 2015. CrossRef
C. Jiang, X. Wang, W. Jian, H. H. Chen, and R. Yong, “Security in space information networks,” IEEE Communications Magazine, vol. 53, no. 8, pp. 82–88, 2015. CrossRef
C. Jiang, C. Yan, and K. J. R. Liu, “Data-driven optimal throughput analysis for route selection in cognitive vehicular networks,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 11, pp. 2149–2162, 2014. CrossRef
C. Jiang, C. Yan, G. Yang, and K. J. R. Liu, “Indian buffet game with negative network externality and non-bayesian social learning,” IEEE Transactions on Systems Man & Cybernetics Systems, vol. 45, no. 4, pp. 609–623, 2013. CrossRef
C. Jiang, Y. Chen, Q. Wang, and K. J. R. Liu, “Data-driven auction mechanism design in iaaS cloud computing,” IEEE Transactions on Services Computing, vol. 11, no. 5, pp. 743–756, 2018. CrossRef
C. Jiang, Y. Chen, Y. H. Yang, C. Y. Wang, and K. J. R. Liu, “Dynamic chinese restaurant game: Theory and application to cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 1960–1973, 2014. CrossRef
C. Jiang, Y. Chen, Y. Gao, and K. J. R. Liu, “Indian buffet game with negative network externality and non-bayesian social learning,” IEEE Transactions on Systems Man & Cybernetics Systems, vol. 45, no. 4, pp. 609–623, 2015. CrossRef
L. Feng, C. Jiang, J. Du, Y. Jian, R. Yong, Y. Shui, and M. Guizani, “A distributed gateway selection algorithm for uav networks,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 22–33, 2017.
Y. Kawamoto, H. Takagi, H. Nishiyama, and N. Kato, “Efficient resource allocation utilizing q-learning in multiple ua communications,” IEEE Transactions on Network Science & Engineering, vol. PP, no. 99, pp. 1–1.
X. Lei, C. Jiang, C. Yan, R. Yong, and K. J. R. Liu, “Privacy or utility in data collection? a contract theoretic approach,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 7, pp. 1256–1269, 2015. CrossRef
L. Zhang, H. Yao, H. Liu, and Z. Zhou, “A novel ultra-wide band signal generation scheme based on carrier interference and dynamics suppression,” Eurasip Journal on Wireless Communications & Networking, vol. 2010, no. 1, p. 10, 2010.
H. Yao, X. Chen, M. Li, P. Zhang, and L. Wang, “A novel reinforcement learning algorithm for virtual network embedding,” Neurocomputing, vol. 284, pp. 1–9, 2018. CrossRef
M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” Advances in Neural Information Processing Systems, vol. 14, no. 6, pp. 585–591, 2009.
G. W. Stewart and J. G. Sun, “Matrix perturbation theory,” 1990.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012, pp. 1097–1105.
C. Jiang, C. Yan, R. Yong, and K. J. R. Liu, “Maximizing network capacity with optimal source selection: A network science perspective,” IEEE Signal Processing Letters, vol. 22, no. 7, pp. 938–942, 2014. CrossRef
K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “A brief survey of deep reinforcement learning,” 2017.
D. Drutskoy, E. Keller, J. Rexford, Scalable network virtualization in software-defined networks, IEEE Internet Computing 17 (2) (2013) 20–27. CrossRef
N. Zhang and H. P. Yao, “Overview of euicc remote management technology,” Telecom Engineering Technics & Standardization, 2012.
R. Jain, S. Paul, Network virtualization and software defined networking for cloud computing: a survey, Communications Magazine IEEE 51 (11) (2013) 24–31. CrossRef
A. Fischer, J. F. Botero, M. T. Beck, H. D. Meer, X. Hesselbach, Virtual network embedding: A survey, IEEE Communications Surveys & Tutorials 15 (4) (2013) 1888–1906. CrossRef
C. Liang, F. R. Yu, Wireless network virtualization: A survey, some research issues and challenges, IEEE Communications Surveys & Tutorials 17 (1) (2015) 358–380. CrossRef
N. M. K. Chowdhury, R. Boutaba, Network virtualization: state of the art and research challenges, IEEE Communications magazine 47 (7) (2009) 20–26. CrossRef
H. Zhang, C. Jiang, J. Cheng, and V. C. M. Leung, “Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks,” Wireless Communications IEEE, vol. 22, no. 3, pp. 92–99, 2015. CrossRef
N. M. M. K. Chowdhury, R. Boutaba, A survey of network virtualization, Computer Networks 54 (5) (2010) 862–876. CrossRef
Y. Zhu, M. Ammar, Algorithms for assigning substrate network resources to virtual network components, in: INFOCOM 2006. IEEE International Conference on Computer Communications. Proceedings, 2007, pp. 1–12.
M. Yu, Y. Yi, J. Rexford, M. Chiang, Rethinking virtual network embedding: substrate support for path splitting and migration, Acm Sigcomm Computer Communication Review 38 (2) (2008) 17–29. CrossRef
L. Xu, C. Jiang, J. Wang, J. Yuan, and Y. Ren, “Information security in big data: Privacy and data mining,” IEEE Access, vol. 2, no. 2, pp. 1149–1176, 2017.
N. M. M. K. Chowdhury, M. R. Rahman, R. Boutaba, Virtual network embedding with coordinated node and link mapping, Proceedings - IEEE INFOCOM 20 (1) (2009) 783–791.
C. Jiang, N. C. Beaulieu, Z. Lin, R. Yong, M. Peng, and H. H. Chen, “Cognitive radio networks with asynchronous spectrum sensing and access,” Network IEEE, vol. 29, no. 3, pp. 88–95, 2015. CrossRef
M. A. Ying-Jie, Z. Zhou, H. E. Wen-Cai, J. Zhang, and H. P. Yao, “Cognitive uwb orthogonal pulses design and its performance analysis,” Transactions of Beijing Institute of Technology, vol. 31, no. 5, pp. 583–588, 2011.
S. Shanbhag, A. R. Kandoor, C. Wang, R. Mettu, T. Wolf, Vhub: Single-stage virtual network mapping through hub location, Computer Networks 77 (2015) 169–180. CrossRef
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al., Mastering the game of go with deep neural networks and tree search, Nature 529 (7587) (2016) 484–489. CrossRef
C. Jiang, C. Yan, K. J. R. Liu, and R. Yong, “Optimal pricing strategy for operators in cognitive femtocell networks,” Wireless Communications IEEE Transactions on, vol. 13, no. 9, pp. 5288–5301, 2014. CrossRef
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, Human-level control through deep reinforcement learning, Nature 518 (7540) (2015) 529. CrossRef
S. Mozer, M. C, M. Hasselmo, Reinforcement learning: An introduction, Machine Learning 8 (3-4) (1992) 225–227.
L. Meng, F. R. Yu, P. Si, H. Yao, E. Sun, and Y. Zhang, “Energy-efficient m2m communications with mobile edge computing in virtualized cellular networks,” in IEEE International Conference on Communications, 2017.
X. Jin, P. Zhang, and H. Yao, “A communication framework between backbone satellites and ground stations,” in International Symposium on Communications & Information Technologies, 2016.
S. Haeri, L. Trajkovic, Virtual network embedding via monte carlo tree search., IEEE Transactions on Cybernetics (99) (2017) 1–12.
R. Mijumbi, J. L. Gorricho, J. Serrat, M. Claeys, F. D. Turck, S. Latre, Design and evaluation of learning algorithms for dynamic resource management in virtual networks, in: Network Operations and Management Symposium, 2014, pp. 1–9.
H. Zhang, C. Jiang, N. C. Beaulieu, X. Chu, X. Wen, and M. Tao, “Resource allocation in spectrum-sharing ofdma femtocells with heterogeneous services,” IEEE Transactions on Communications, vol. 62, no. 7, pp. 2366–2377, 2014. CrossRef
M. Thomas, E. W. Zegura, Generation and analysis of random graphs to model internetworks, College of Computing volume 63 (4) (1994) 413–442(30).
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al., Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:1603.04467.
Q. Chao, C. Zhao, H. Yao, F. Xu, and F. R. Yu, “Why did you opt to switch off me? big data for green software defined networking,” in Globecom Workshops, 2017.
L. Bottou, Online algorithms and stochastic approximations, in: D. Saad (Ed.), Online Learning and Neural Networks, Cambridge University Press, Cambridge, UK, 1998, revised, October 2012.
- Intelligent Network Resource Management
- Chapter 5