Data centers, the critical infrastructure underpinning Cloud computing, often employ Software-Defined Networks (SDN) to manage cluster, wide-area and enterprise networks. As the network forwarding in SDN is dynamically programmed by controllers, it is crucial to ensure that the controller intent is correctly translated into underlying forwarding rules. Therefore, detecting and locating forwarding anomalies in SDN is a fundamental problem in production networks. Existing research proposals, roughly categorized into probing-based, packet piggybacking-based, and flow statistics analysis-based, either impose significant overhead or do not provide sufficient coverage for certain forwarding anomalies. In this article, we propose${\sf FADE}$, a controllable and passive measuring scheme to simultaneously deliver detection efficiency and accuracy.${\sf FADE}$first analyzes the entire network topology and flow rules, and then computes a minimal set of flows that can cover all forwarding rules. For each selected network flow,${\sf FADE}$decides the optimal number of monitoring positions on its path (much less than total number of hops), and installs dedicated rules to collect flow statistics.${\sf FADE}$controls the installation and expiration of these rules, along with unique flow labels, to guarantee the accuracy of collected statistics, based on which${\sf FADE}$algorithmically decides whether a forwarding anomaly is detected, and if so it further locates the anomaly. On top of${\sf FADE}$, we propose${\sf iFADE}$(a more scalable version of${\sf FADE}$) to further optimize the usage and deployment of dedicated measurement rules.${\sf iFADE}$achieves over 40 percent rule reduction compared with${\sf FADE}$. We implement a prototype of both${\sf FADE}$and${\sf iFADE}$in about 12000 lines of code and evaluate the prototype extensively. The experiment results demonstrate${\sf (i)}$${\sf FADE}$and${\sf iFADE}$are accurate, e.g., they achieve over 95 percent true positive rate and 99 percent true negative rate in anomaly detection;${\sf (ii)}$${\sf FADE}$and${\sf iFADE}$are lightweight, e.g., they reduce the overhead of control messages compared with state-of-the-art by about 50 and 90 percent, respectively.

Packet forwarding anomaly is an abnormal network state where flows are forwarded along wrong paths. Current practice of forwarding anomaly detection in Software Defined Networks (SDN) is achieved by sending probing packets or analyzing flow statistics. However, these approaches are not effective and efficient. For example, the probing approaches cannot capture all attacks, and the statistics approaches induce high communication overheads since they collect statistics of all flows. In order to address these issues, we propose a novel scheme called FADE. FADE detects forwarding anomalies by accurately analyzing flow statistics of a minimal set of flows. It generates a small number of dedicated flow rules associated with these flows to accurately measure their statistics. Moreover, it controls the installing and timeout of these dedicated flow rules so that all dedicated flow rules generated for the same flow operate on the same set of packets. Therefore, it achieves high efficiency and accuracy in anomaly detection. We prototype FADE and implement it as an application in Opensource controller, Floodlight, and evaluate the performance by Mininet experiments. The experiment results show that FADE can detect almost all forwarding anomalies and only reduces the throughput by 4%.