@Article{cmc.2021.017475,
AUTHOR = {Daniel Godfrey, Beom-Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae-Eung Sung, Ki-Il Kim},
TITLE = {Q-Learning Based Routing Protocol for Congestion Avoidance},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {3},
PAGES = {3671--3692},
URL = {http://www.techscience.com/cmc/v68n3/42511},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2021.017475}
}