RLSketch: Scaling Reinforcement Learning for Adaptive and Automate Anomaly Detection in Network Data Streams When network is undergoing problems, such as DDoS attack, component failures, etc., the detection of heavy flows (e.g. heavy hitters and heavy changers) is much more critical. However, it has been increasing challenging to ensure the accurate detection of heavy flows while dealing with massive network traffic volume, diversified traffic distribution and the stringent memory requirement. Although recent research efforts like LDSketch are scalable for diverse network traffic, they depend on excessive memory to maintain high accuracy, such that they fail to work well when the memory is limited. We propose RLSketch, a adaptive sketch using reinforcement learning in detecting heavy flows. It predicts potential heavy flows based on the statistics of network traffic, to achieve both high accuracy and scalability with minor memory. Tracedriven evaluation shows that RLSketch achieves higher accuracy than stateoftheart sketchbased technologies with up to 17.79xc3x97 accuracy gain, while maintaining high robustness in extreme conditions.

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.