DeepRL: Deep Reinforcement Learning for MarkingAware via perPort in Data Centers In this paper, we propose DeepRLxe2x80x94a marking decision with Deep Reinforcement Learning (DRL) via perport for solving the erroneously marking problems in multiqueue multi service scenarios of Data Center Networks (DCNs). We formulate the statement as a DRL problem and use Deep Neural Network (DNN) to achieve the best possible policy for the agent. In this way we can model the complex DCNs in order to obtain the optimal threshold in the output port when the marked packets from queue buffers are not a concern. Unlike prior research that focused on mathematical models or used machine learning in DCN, DeepRL is a novel DRL based method, which optimizes the real value of threshold with continues action space. Thus, this fact makes our work incomparable with previous research. To the best of our knowledge, we are the first to discuss the problem with DRL and DNN. Simulation results demonstrate that DeepRL utilizes the buffer capacity at exactly 30% and achieves near optimal flow completion time.

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.