Abstract:
In this paper, we propose a Q-Learning approach
for load balancing in Software Defined Networks to reduce the
number of Unsatisfied Users in a 5G network. This solution
integrates Q-Learning techniques with a fairness function to
improve the user experience at peak traffic conditions. With
typical high rates offered by 5G and future networks single
user behavior shall have a significant impact on the Quality
of Service (QoS) on the rest of the users. Therefore, we are in
need of responsive networks based on their utilization and on
the number of users occupied. In this paper we classify users
into different groups and normalize the resources to provide
the best QoS. The simulation results verify the improvement in
terms of the number of Unsatisfied Users and of the connections
dropped. Additionally, it enhances per-flow resource allocation
while avoiding over-utilization of certain network resources. In a
nutshell, this proposal will serve any future network with high
traffic conditions to deliver the best QoS to their end users.
Citation:
D. Tennakoon, S. Karunarathna and B. Udugama, "Q-learning Approach for Load-balancing in Software Defined Networks," 2018 Moratuwa Engineering Research Conference (MERCon), 2018, pp. 1-6, doi: 10.1109/MERCon.2018.8421895.