IPro: An approach for intelligent SDN monitoring Abstract   Traffic Monitoring assists in achieving network stability by observing and quantifying its behavior. A proper traffic monitoring solution requires the accurate and timely collection of flow statistics. Many approaches have been proposed to monitor SoftwareDefined Networks. However, these approaches have some disadvantages. First, they are unconcerned about the tradeoff between probing interval and Monitoring Accuracy (MA). Second, they lack intelligent mechanisms intended to optimize this tradeoff by learning from network behavior. This paper introduces an approach, called IPro, to address these shortcomings. Our approach comprises an architecture based on the KnowledgeDefined Networking paradigm, an algorithm based on Reinforcement Learning, and an IPro prototype. In particular, IPro uses Reinforcement Learning to determine the probing interval that keeps Control Channel Overhead (CCO) and the Extra CPU Usage of the Controller (CUC) within thresholds. An extensive quantitative evaluation corroborates that IPro is an efficient approach for SDN Monitoring regarding CCO, CCU, and MA.

Reinforcement LearningDriven QoSAware Intelligent Routing for SoftwareDefined Networks Softwaredefined network (SDN) is an emerging computer networking technology that disjoints the data forwarding from the centralized control and enables a highly manageable and flexible networking paradigm. There has been intensive research developed for efficient routing and resource allocation for SDNs. However, there still remain essential challenges to achieve situationawareness networking management to ensure the applicationdriven QualityofService (QoS) even in the presence of cyber attacks. To address this issue, in this paper, we exploit reinforcement learning (RL) technologies to develop a situationawareness and intelligent networking management from the perspective of routing management. The performance of our proposed RLenabled routing management method is evaluated in the simulation sections by considering various scenarios.