Background
Software-defined networking is gaining popularity day by day. There exist different types of SDN controllers that enable network administrators to dynamically optimize network resources and provide flow-level quality of service (QoS) provisioning. This QoS routing in SDN needs to be investigated to improve end-to-end throughput. Traditional methods exist but those methods have certain limitations when dealing with multiple QoS parameters.

Challenges
To deal with multiple QoS parameters and to solve this complex problem DRL methods have become very popular to be used. Because these methods have the ability to learn from experience that allows avoiding the development of large accurate mathematical models. But due to the use of Q-learning, different DRL approaches do not perform well when the routing complexity increases.

Goal
To solve the problem, this project aims to investigate the task of quality of service (QoS) routingin software-defined networks (SDN).

Proposed Methodology
To achieve the above-mentioned goal, this project uses different QoS parameters such as delay,bandwidth, loss, and cost. For this purpose, a new DRL solution for greedy online QoS routing in SDN is implemented. This DQR utilizes a dueling deep Q-network with prioritized experience replay to compute a path for any source-destination pair request in the presence of multiple QoS metrics. In this case, we consider SDN which consists of three layers. This architecture of SDN is mentioned in the Figure below.