Predictive analysis for race detection in software-defined networks

作     者：Gongzheng LU Lei XU Yibiao YANG Baowen XU 

作者机构：State Key Laboratory of Novel Software Technology Nanjing University School of Computer Engineering Suzhou Vocational University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2019年第62卷第6期

页      面：34-53页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Basic Research Program of China (973 Program) (Grant No. 2014CB340702) National Natural Science Foundation of China (Grant Nos. 91418202, 61472178, 91318301) National Science Foundation for Young Scientists of China (Grant No. 61702256) 

主　　题：constraint solving predictive analysis race detection software-defined networks 

摘      要：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.