Ensuring the availability of cloud computing services always concerns both service providers and end users. Therefore, the system always needs precautions for unexpected cases. Accordingly, cloud computing services must be capable of identifying faults and behaving appropriately when it is abnormal to ensure the smoothness as well as the service quality. In this study, we propose a fault detection method for multi-tier web application in cloud computing deployment environment based on the Fuzzy One-class support vector machine and Exponentially Weighted Moving Average method. And then, the suspicious metrics are located by using feature selection method which based on Random Forest algorithm. To evaluate our approach, a multi-tier application is deployed by a transnational web e-Commerce benchmark by using TPC-W (TPC Benchmark™ W, simulates the activities of a business oriented transaction web server in a controlled internet commerce environment) in private cloud and then it is injected typical faults. The effectiveness of the fault detection and diagnosis are demonstrated in experiment results.

中文翻译：

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一种面向云计算多层应用的故障检测与诊断方法

确保云计算服务的可用性始终涉及服务提供商和最终用户。因此，系统总是需要针对意外情况采取预防措施。因此，云计算服务必须能够识别故障并在出现异常时做出适当的行为，以保证服务的流畅性和服务质量。在这项研究中，我们提出了一种基于模糊一类支持向量机和指数加权移动平均方法的云计算部署环境中多层Web应用程序的故障检测方法。然后，利用基于随机森林算法的特征选择方法对可疑度量进行定位。为了评估我们的方法，使用 TPC-W（TPC Benchmark™ W，在私有云中的受控互联网商务环境中模拟面向业务的交易 Web 服务器的活动，然后注入典型故障。实验结果证明了故障检测和诊断的有效性。