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Optimal Method for Controller Placement Problem in Sdn Using Machine Learning Techniques

Mirhosseini, Hossein | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56951 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Fazli, Mohammad Amin
  7. Abstract:
  8. Software-defined networking (SDN) is a network management approach that allows centralized control of the network independently from network hardware. As a network management method, SDN provides many capabilities not traditionally found in existing hardware-based networks. However, one of the significant challenges of SDN is the placement of the controller within the network. In SDN, the central controller must be able to efficiently route all data flows separately to end devices. Therefore, the placement of the controller in the network is crucial. However, controller placement in the network poses a major challenge as it needs to be appropriately and optimally positioned to enhance performance. Various methods such as genetic algorithms, adaptive algorithms, machine learning algorithms, etc., have been used to address this challenge. In this research, a novel approach to switch clustering and dynamic controller placement using reinforcement learning and neural networks is introduced. For this purpose, a two-layer model with a root controller and domain controllers is utilized. The proposed method can optimize metrics such as control latency, control load, intra-cluster delay, and operational power within the cluster. Simulations demonstrate that this approach outperforms existing methods
  9. Keywords:
  10. Software Defined Networks (SDN) ; Machine Learning ; Optimization ; Reinforcement Learning ; Load Balancing ; Controller Placement

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