Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning

Abstract

Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it is challenging to choose a proper degree of traffic flow handling granularity while proactively protecting forwarding devices from getting overloaded. In this paper, we propose a novel traffic flow matching control framework called Q-DATA that applies reinforcement learning in order to enhance the traffic flow monitoring performance in SDN based networks and prevent traffic forwarding performance degradation. We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity. It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm. Next, we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require. Our novel approach is realized as a REST SDN application and evaluated in an SDN environment. Through comprehensive experiments, the results show that-compared to the default behavior of common SDN controllers and to our previous DATA mechanism-the new Q-DATA framework yields a remarkable improvement in terms of traffic forwarding performance degradation protection of SDN switches while still providing the most detailed traffic flow information on demand.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture

“…Researchers are focusing on the application of such techniques in the field of networks. ML has found varied uses in the area of SDN, including traffic engineering [17,18], resource management [19,20], intrusion detection systems [21,22], and other security objectives [23,24]. In this regard, Akyildiz et al [25] presented the state of the art for traffic engineering in SDN/OpenFlow networks.…”

Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention

“…To improve flow matching in SDN-based networks, [36] proposed a Q-learning integrated Q-DATA framework that uses support vector machine (SVM) algorithm to detect the performance status of forwarding devices. The Hping3 tool is used in 5 hosts to generate random traffic between destination servers and hosts.…”

Traffic Engineering in Software-defined Networks using Reinforcement Learning: A Review

“…Phan et al [29] proposed the Q-learning algorithm in maximizing traffic flow monitoring in SDN switches. It embeds a Support Vector Machine (SVM) [49] algorithm in the application plane of the SDN architecture to predict the performance degradation of the switches as the episode progresses.…”

“…2) RL agent in Application Plane: For easier system failure checks in SDN, [29] [71] [83] TE frameworks situate the RL agent in the application plane. The Q-DATA [29] framework architecture has a built-in forwarding application located in the control plane and a Q-DATA application residing in the SDN application plane. Initially, the built-in forwarding application module is instructed by the Q-DATA application through a REST API to apply the Full Matching Scheme (FMS) strategy at the switches.…”