基于数据流异常监测的软件容错纠错方法

Software Fault Tolerance and Fault Rectification Approach Based on Data Flow Abnormity Supervision

针对可能出现的软件系统故障,提出了一种新的基于数据流异常监测、软件看门狗技术和回卷恢复技术的软件容错纠错方法。该方法定期对目标程序进行备份,通过提取目标程序中的一组相关变量建立数据流分析模型,利用数据流的异常检测排查出其中的离群点即出错点。提出了上述软件容错纠错策略的实现框架、操作流程,研究了基于最小二乘支持向量机的二元回归模型和离群点检测算法。以二元函数为例,对该文的二元回归模型和离群点检测算法进行了仿真研究,仿真结果验证了回归模型的正确性和离群点检测算法的有效性。

Aiming at possible failure in software system, a novel software fault tolerance and fault rectification approach is proposed based on data flow abnomity supervision, software watch dog technique and rollback recover technique. This approach backups the object program regularly, establishes data flow analysis model by extracting a group of related variables in object program, and filtrates ＇outliers＇ from the data flow by using data flow abnormity detection method. Implementation framework, operation procedure for above software fault tolerance and fault rectification strategy are presented. Binary regression model based on least square-support vector machine and ＇outliers＇ detection algorithm are also studied. Simulation results demonstrate the correctness of the binary regression model and the validity of the ＇outliers＇ detection algorithm.