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.

Intelligent Routing based on Deep Reinforcement Learning in SoftwareDefined DataCenter Networks In softwaredefined datacenter networks, Elephant flowMice flowCoflow coexist and multiple resources (bandwidth, cache and computing) coexist. However, the conventional routing methods cannot overcome the large gap between different performance requirements of flow and efficient resource allocation. Therefore, this paper proposes DRLR (Deep Reinforcement Learningbased Routing) to bridge this gap. First, we recombine multiple resources (nodexe2x80x99s cache, linkxe2x80x99s bandwidth) by quantifying the contribution score of them reducing the delay. This actually converts the performance requirements of flow into resource requirements of this flow, hence, the routing problem can be converted into a jobscheduling problem in resource management. Second, a DRL agent deployed on an SDN controller continually interacts with the network for adaptively performing reasonable routing according to the network state, and optimally allocating network resources for traffic. We employ Deep QNetwork (DQN) and Deep Deterministic Policy Gradient (DDPG) to build the DRLR. Finally, we demonstrate the effectiveness of DRLR through extensive simulations. Benefitted from continually learning with a global view, DRLR can improve throughput highest up to 40% and flow completion time highest up to 47% over OSPF. DRLR can improve the load balance of link highest up to 18.8% over OSPF. Additionally, DDPG has better performance than DQN.