Source of Publication
Computers, Materials and Continua
The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state, Q-value, and reward function to set the desired path toward the destination. A new reward function that consists of a buffer occupancy, link reliability and hop count is considered.Moreover, look ahead algorithm is employed to update the Q-value with values within two hops simultaneously. This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account, accordingly. Finally, the simulation results presented approximately 20% higher packet delivery ratio and 15% shorter end-to-end delay, compared to those with the existing scheme by avoiding congestion adaptively.
Congestion-aware routing, Q-learning, Reinforcement learning, Software defined networks
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Godfrey, Daniel; Kim, Beom Su; Miao, Haoran; Shah, Babar; Hayat, Bashir; Khan, Imran; Sung, Tae Eung; and Kim, Ki Il, "Q-learning based routing protocol for congestion avoidance" (2021). All Works. 4212.
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Open Access Type
Gold: This publication is openly available in an open access journal/series