Research On Diagnostic Methods For Web Service Faults

With the continuous development of web service technology, and the successful deployment of large-scale web service applications, many challenges have been posed to the quality of service software by service-oriented computing because of its dynamic, opennes, autonomy and sociality nature. In building high-reliable service applications under the open environment, one of the critical challenges is how to localize faulty service quickly and exactly through the observed symptoms or throwed exceptions, how to analyze the reasons of its misbehavior and help service engine restore the normal process as soon as possible.Based on comparative analysis of the state-of-the-art research, this thesis works on problems of incomplete model, diagnosis uncertainty and complexity, including the following:(1) To minimize failure impact on services and application execution, we present a diagnostic architecture of web service under the open environment. As an important bridge linking diagnosis service to web service in the architecture, the exception handler is responsible for catching the thrown exceptions, collecting the diagnostic information, selecting and invoking the appropriate diagnosis service. According to the diagnostic result of diagnosis service, the exception handler is able to get the corresponding recovery strategy from recovery selector and recover the service process from faulty effects.(2) To improve the diagnostic accuracy and efficiency, we propose a complete BPN model-based perfecting diagnosis method to diagnose faults in service process. The method defines the Petri nets-based process model for each activity in BPEL (complete BPN model). To raise diagnostic efficiency, the method optimizes the diagnostic order of service activities by computing the faulty probability of activities. The experimental results show that the method is more accuracy and costs less time than the existing MBD methods. (3) To eliminate the defects in the definition of composite service process, we propose a statistic diagnosis method based on service dependence graph. The service dependence graph-based diagnosis is an important feature of the method which extracts the dependence relations from the process specification to simplify the analysis of dependence relations between activities. The method is able to identify the incorrect activities and explain the root causes based on the differences between successful and failed executions of historical data. Experimental results show that our method is effective in diagnosing faults in web service composition of various scales.(4) To localize the faults of undefined service behaviors in service process, we propose a service execution matrix-based Bayes diagnosis method. On the basis of modeling the service execution matrix by historical data, the method combines multi-faults logic reasoning approach with the statistical techniques to obtain the diagnosis candidates. By applying Bayes’formula to compute the fault probability of each diagnosis candidate, the method is able to obtain an asymptotically optimal diagnosis with increasing historical data. Experiments are conducted to evaluate the effectiveness of the method in diagnosing the web services with multi-faults.(5) We present a hidden markov model-based (HMM) difference comparison diagnosis method for perfecting the service model and minimizing noise data impact. The method models hidden markov model by combining historical data into service process definition. On the basis of improving the decoding algorithms in HMM, the method is able to find a correct execution trace which has the maximum likelihood with the exception execution trace and localize the service faults by comparing the differences between them. The experimental results show that the method is effective and robust to various noises in diagnosing the faults of web services.