DQS: A QoS-driven routing optimization approach in SDN using deep reinforcement learning.

In recent decades, the exponential growth of applications has intensified traffic demands, posing challenges in ensuring optimal user experiences within modern networks. Traditional congestion avoidance and control mechanisms embedded in conventional routing struggle to promptly adapt to new-generation networks. Current routing approaches risk-averse outcomes such as (1) scalability constraints, (2) high convergence times, and (3) congestion due to inadequate real-time traffic prioritization. To address these issues, this paper introduces a QoS-Driven Routing Optimization in Software-Defined Networking (SDN) using Deep Reinforcement Learning (DRL) to optimize routing and enhance QoS efficiency. Employing DRL, the proposed DQS optimizes routing decisions by intelligently distributing traffic, guided by a multi-objective function-driven DRL agent that considers both link and queue metrics. Despite the complexity of the network, DQS sustains scalability while significantly reducing convergence times. Through a Docker-based Openflow prototype, results highlight a substantial 20-30% reduction in end-to-end delay compared to baseline methods. • DQS tackles scalability and congestion with a multi-objective loss function, integrating CoS into routing decisions to make informed decisions. • DQS employs a seven-parameter traffic classifier with ML techniques, efficiently categorizing CoS and ensuring mice and elephant traffic. • The paper presents DQS, employing DRL to optimize routing decisions, minimize delay and loss, and maximize bandwidth use across links and queues. • We evaluate DQS with a Docker-based SDN prototype, showing its adaptability, scalability, and responsiveness to traffic changes and QoS demands. • Results show 20-30% delay reduction and 14% processing time improvement over state-of-the-art algorithms, enhancing network performance. [ABSTRACT FROM AUTHOR]