Congestion prevention mechanism based on Q-leaning for efficient routing in SDN

Kim, Seonhyeok; Son, Jaehyeok; Talukder, Ashis; Hong, Choong Seon

doi:10.1109/icoin.2016.7427100

Cited by 37 publications

(24 citation statements)

References 8 publications

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“…RL method is an effective method to solve this problem. The authors in [15] explored the less congested paths in the SDN network that were focussed on the Q-learning approach. Compared with Dijkstra and Extended Dijkstra, this method gets better results when the size of the transmitted data is increased.…”

Section: Related Workmentioning

confidence: 99%

Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

et al. 2021

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Section: Related Workmentioning

confidence: 99%

Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

et al. 2021

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“…Kim et al [11] discusses a way to solve congestion caused by Dijkstra's shortest path as it does not consider bandwidth. In an SDN network, QL is trained to select a new path if the congestion threshold on the original path has been reached.…”

Section: Q N (S A) = Q(s A)+α[r(s A)+γm Axq(s a )−Q(s A)]mentioning

confidence: 99%

The Performance of Q-Learning within SDN Controlled Static and Dynamic Mesh Networks

Harewood-Gill

Martin

Nejabati

2020

2020 6th IEEE Conference on Network Softwarization (NetSoft)

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Current infrastructures are reaching the point where existing networking methods are unable to cope with the exponential growth of traffic and Quality of Service (QoS) requirements. New techniques are necessary to keep pace. One such technique, Software-Defined Networking (SDN) uses a central controller to program many individual network devices. However, SDN uses heuristic algorithms that do not always select the optimal path. This paper looked at creating three Q-Routing algorithms leveraging SDN and Mesh network topologies. Two algorithms used one network metric each (Latency and Bandwidth) and the third used multiple metrics. Results showed that the single metric Q-Routing algorithms on average performed as well as the K-Shortest Path versions while Q-Routing with multiple network metrics failed to match K-Shortest Path (different combination of metrics means these algorithms are not comparable). Results also showed that Q-Routing was able to calculate paths faster than K-Shortest Path in both static and dynamic networks.

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“…In this context, it is worth noting that RL [165][166][167][168][169][170][171][172][173][174][175][176][177] was widely applied in SDN paradigm for routing and adaptive video streaming.…”

Section: Rl In Sdnmentioning

confidence: 99%

“…Kim et al . [168] investigated congestion prevention based on Q‐learning approach. Compared with Dijkstra's algorithm and extended Dijkstra's algorithm, the proposed approach showed better results when the size of transmitted data increases.…”

Section: Artificial Intelligence In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

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Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

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Congestion prevention mechanism based on Q-leaning for efficient routing in SDN

Cited by 37 publications

References 8 publications

Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

The Performance of Q-Learning within SDN Controlled Static and Dynamic Mesh Networks

Artificial intelligence enabled software‐defined networking: a comprehensive overview

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