%0 Journal Article %1 noauthororeditor %A Fan Shen, Levi Perigo %D 2023 %J International Journal of Next-Generation Networks (IJNGN) %K DQN SDN controller deep latency learning placement problem reinforcement %N 1 %P 13 %R https://doi.org/10.5121/ijngn.2023.15101 %T Dynamic SDN Controller Placement based on Deep Reinforcement Learning %U https://aircconline.com/ijngn/V15N1/15123ijngn01.pdf %V 15 %X Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.

@article{noauthororeditor, abstract = {Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.}, added-at = {2023-04-05T07:10:30.000+0200}, author = {Fan Shen, Levi Perigo}, biburl = {https://www.bibsonomy.org/bibtex/267603f2f02eba056090386b5a281ec5e/josephjonse}, doi = {https://doi.org/10.5121/ijngn.2023.15101}, interhash = {6568e7c23c9045996995eb4ca6e3e340}, intrahash = {67603f2f02eba056090386b5a281ec5e}, journal = {International Journal of Next-Generation Networks (IJNGN)}, keywords = {DQN SDN controller deep latency learning placement problem reinforcement}, language = {English}, month = {3}, number = 1, pages = 13, timestamp = {2023-04-05T07:10:30.000+0200}, title = {Dynamic SDN Controller Placement based on Deep Reinforcement Learning}, url = {https://aircconline.com/ijngn/V15N1/15123ijngn01.pdf}, volume = 15, year = 2023 }