Automatic Inference of HighLevel Network Intents by Mining Forwarding Patterns There is a semantic gap between the highlevel intents of network operators and the lowlevel configurations that achieve the intents. Previous works tried to bridge the gap using verification or synthesis techniques, both requiring formal specifications of the intended behavior which are rarely available or even known in the real world. This paper discusses an alternative approach for bridging the gap, namely to infer the highlevel intents from the lowlevel network behavior. Specifically, we provide Anime, a framework and a tool that given a set of observed forwarding behavior, automatically infers a set of possible intents that best describe all observations. Our results show that Anime can infer highquality intents from the lowlevel forwarding behavior with acceptable performance.

Toward Comprehensive Network Verification: Practices, Challenges and Beyond Guaranteeing that large scale networks are always operated as policy goals dictate is critical for network availability, security, and performance. The problem is challenging, as it should not only assure the correctness of policy configurations across various network devices, but also guarantee the faithfulness of policy enforcement on actual packet forwarding behaviors. In this article, we provide a systematic overview of the direction of network verification, which covers both verification of network configurations and assurance of device actual behaviors. In particular, we summarize stateoftheart designs with focuses from early stateless data planes with switchesrouters to more recent stateful ones containing middleboxes. After analyzing and comparing these works, we also specify future directions in the verification of stateful networks.