Evolving Switch Architecture toward Accommodating InNetwork Intelligence Motivated by the breakthroughs of AI in both theory and applications, we are perceiving a great potential for network innovations from a new dimension of intelligence. However, as the engine of networks, switches are designed as dumb network elements with the sole purpose of forwarding network packets, and thus a barrier against the entrance of AI as an intrinsic part of the network is unconsciously erected. This article proposes an evolved switch architecture aimed at breaking this barrier and accommodating innetwork intelligence. We enhance the current switch architecture by embedding an intelligence plane, which is externalized as an intelligent computation pluggable module of an evolved switch. The module employs an integrated solution of X86 CPUGPUDPDK, which provides a highperformance and highthroughput open platform for hosting innetwork intelligence. We further conceive a flexible processing framework for an intelligent traffic measurement, recognition, and traffic regime. We also carry out extensive experiments to demonstrate the capability of the evolved switch by deploying a prototype in a campus network, with two promising application scenarios: innetwork application identification and innetwork anomaly detection.

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.