2018 Moratuwa Engineering Research Conference (MERCon) 2018
DOI: 10.1109/mercon.2018.8421895
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Q-learning Approach for Load-balancing in Software Defined Networks

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Cited by 8 publications
(3 citation statements)
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“…The optimal path chosen is delivered to the OpenVSwtiches (OVS) after Qrouting by the controller during network congestion. To balance the network load in SDN, [53] proposed a Q-learning approach to reduce the number of unsatisfied users in a 5G network architecture. The researchers used a flow admission control technique with a fairness function to enhance the perflow resource allocation in the network.…”
Section: : End Loopmentioning
confidence: 99%
“…The optimal path chosen is delivered to the OpenVSwtiches (OVS) after Qrouting by the controller during network congestion. To balance the network load in SDN, [53] proposed a Q-learning approach to reduce the number of unsatisfied users in a 5G network architecture. The researchers used a flow admission control technique with a fairness function to enhance the perflow resource allocation in the network.…”
Section: : End Loopmentioning
confidence: 99%
“…In the literature, there are only a few studies on AI-based SDN-load balancing. [18][19][20][21][22][23] These are as follows:…”
Section: Related Workmentioning
confidence: 99%
“…Tennakoon et al 20 propose a Q‐Learning approach for load balancing in SDN to reduce the number of unsatisfied users in the 5G network. The Q‐learning algorithm controls the flow of a user to the base station that gives the maximum reward rather than the minimum value for the fairness function.…”
Section: Related Workmentioning
confidence: 99%