Abstract
In this study, we propose a smart routing and bandwidth allocation scheme, named Intelligent Routing and Bandwidth Allocation System with Reinforcement Learning (IRBRL), which mainly developed based on reinforcement learning techniques and SDN controller is responsible for creating and maintaining routing policies, including dynamic routing and link bandwidth allocation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Weighted Fair Queueing.
- 2.
Quality Of Experience.
References
Tanenbaum, A.S., Wetherall, D.J.: Computer Networks, 5th edn. Prentice Hall Press, Upper Saddle River (2010)
Hawkinson, J.A., Bates, T.J.: Guidelines for creation, selection, and registration of an Autonomous System (AS). RFC 1930 (1996). https://doi.org/10.17487/RFC1930. https://rfc-editor.org/rfc/rfc1930.txt
Moy, J.: OSPF Version 2. RFC 2328 (1998). https://doi.org/10.17487/RFC2328. https://rfc-editor.org/rfc/rfc2328.txt
Dijkstra, E.W.: Numer. Math. 1(1), 269 (1959). https://doi.org/10.1007/BF01386390
Savage, D., Ng, J., Moore, S., Slice, D., Paluch, P., White, R.: Cisco’s enhanced interior gateway routing protocol (EIGRP). RFC 7868 (2016). https://doi.org/10.17487/RFC7868. https://rfc-editor.org/rfc/rfc7868.txt
Hopps, C.: Analysis of an equal-cost multi-path algorithm. RFC 2992 (2000). https://doi.org/10.17487/RFC2992. https://rfc-editor.org/rfc/rfc2992.txt
Floyd, S., Allman, M.: Comments on the usefulness of simple best-effort traffic. RFC 5290 (2008). https://doi.org/10.17487/RFC5290. https://rfc-editor.org/rfc/rfc5290.txt
Azzouni, A., Boutaba, R., Pujolle, G.: NeuRoute: predictive dynamic routing for software-defined networks. In: 13th International Conference on Network and Service Management, pp. 1–6 (2017). https://doi.org/10.23919/CNSM.2017.8256059
Even, S., Itai, A., Shamir, A.: On the complexity of time table and multi-commodity flow problems. In: 16th Annual Symposium on Foundations of Computer Science (SFCS 1975), pp. 184–193 (1975). https://doi.org/10.1109/SFCS.1975.21
Boyan, J.A., Littman, M.L.: Packet routing in dynamically changing networks: a reinforcement learning approach. In: Proceedings of the 6th International Conference on Neural Information Processing Systems, NIPS 1993, pp. 671–678. Morgan Kaufmann Publishers Inc., San Francisco (1993). http://dl.acm.org/citation.cfm?id=2987189.2987274
Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet-Ros, P., Alarcón, E., Solé, M., Muntés-Mulero, V., Meyer, D., Barkai, S., Hibbett, M.J., Estrada, G., Ma’ruf, K., Coras, F., Ermagan, V., Latapie, H., Cassar, C., Evans, J., Maino, F., Walrand, J., Cabellos, A.: SIGCOMM Comput. Commun. Rev. 47(3), 2 (2017). https://doi.org/10.1145/3138808.3138810. http://doi.acm.org/10.1145/3138808.3138810
He, K.: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5353–5360 (2014). https://arxiv.org/abs/1412.1710
Qiu, C., Cui, S., Yao, H., Xu, F., Yu, F.R., Zhao, C.: Futur. Gener. Comput. Syst. 92, 43 (2019). https://doi.org/10.1016/j.future.2018.09.023
Amiri, R., Mehrpouyan, H.: Self-organizing mm wave networks: a power allocation scheme based on machine learning. In: 11th Global Symposium on Millimeter Waves, pp. 1–4 (2018). https://doi.org/10.1109/GSMM.2018.8439323
Uzakgider, T., Cetinkaya, C., Sayit, M.: Comput. Netw. 92, 357 (2015). https://doi.org/10.1016/j.comnet.2015.09.027
Lin, S., Akyildiz, I.F., Wang, P., Luo, M.: QoS-aware adaptive routing in multi-layer hierarchical software defined networks: a reinforcement learning approach. In: IEEE International Conference on Services Computing, pp. 25–33 (2016). https://doi.org/10.1109/SCC.2016.12
Yao, H., Mai, T., Xu, X., Zhang, P., Li, M., Liu, Y.: IEEE Internet Things J., 1 (2018). https://doi.org/10.1109/JIOT.2018.2859480
Feature scaling. https://en.wikipedia.org/wiki/Feature_scaling
Li, D., Shang, Y., Chen, C.: Software defined green data center network with exclusive routing. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communication, pp. 1743–1751 (2014). https://doi.org/10.1109/INFOCOM.2014.6848112
Hsun, L.Y. https://github.com/THU-DBLAB/IRBRL
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, YH., Leu, FY. (2020). Dynamic Routing and Bandwidth Provision Based on Reinforcement Learning in SDN Networks. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-44041-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44040-4
Online ISBN: 978-3-030-44041-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)