Predictive analysis for race detection in software-defined networks

Race condition remains one kind of the most common concurrency bugs in software-defined networks (SDNs). The race conditions can be exploited to lead to security and reliability risks. However, the race conditions are notoriously difficult to detect. The existing race detectors for SDNs have limited detection capability. They can only detect the races in the original traces (observed traces) and cause false negatives. In this study, we present a predictive analysis framework called SDN-predict for race detection in SDNs. By encoding the order between the specified network events in SDNs as constraint, we formulate race detection as a constraint solving problem. In addition to detecting the races in the original trace, our framework can also detect the races in the feasible traces got from reordering the events in the original trace while satisfying the consistency requirements of trace. Moreover, we formally prove that our predictive analysis framework is sound and can achieve the maximal possible detection capability for any sound dynamic race detector with respect to the same trace. We evaluate our framework on a set of traces collected from three SDN controllers (POX, Floodlight, ONOS), running 5 representative applications including reactive and proactive applications in large networks, on three different network topologies. These experiments show that our framework has higher race detection capability than exisiting SDN race detector-SDNRacer, and detects more 1173 races. These 1173 races were previously undetected and confirmed by checking the race graphs.