| Title | Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Phan, Trung V., Islam, Syed Tasnimul, Nguyen, Tri Gia, Bauschert, Thomas |
| Conference Name | 2019 15th International Conference on Network and Service Management (CNSM) |
| Keywords | centralised control, centralized network control, centralized network management, computer network management, computer network reliability, computer network security, control framework, control plane, Control System, control systems, data plane, Degradation, forwarding performance status, learning (artificial intelligence), Monitoring, Network Statistics and Software-Defined Networking, optimal traffic flow matching policy, policy formulation, proactive forwarding device protection, pubcrawl, Q-DATA framework, Q-learning algorithm, reinforcement learning, reinforcement learning approach, Resiliency, REST SDN application, Scalability, SDN based networks, SDN controllers, SDN environment, SDN switches, SDN-based traffic flow, SDN-based traffic flow matching control system, security analysis, Security by Default, software defined networking, software-defined networking, Software-Defined Networks, support vector machine based algorithm, Support vector machines, telecommunication traffic, traffic flow granularity, traffic flow information, traffic flow monitoring, traffic forwarding performance degradation protection |
| 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. |
| DOI | 10.23919/CNSM46954.2019.9012727 |
| Citation Key | phan_q-data_2019 |
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning