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To ensure the stable running of systems and services in a data center, O&M engineers need to collect and monitor Key Performance indicators (KPIs) generated during system and service running. Traditional performance anomaly detection methods based on thresholds and rules are suitable for simple KPI monitoring. Unfortunately, when there are significant differences between KPI values at different times of the day, or when there are significant fluctuations due to different usage patterns, it is difficult to find appropriate threshold levels that are gradually no longer appropriate for highly dynamic systems and businesses. With the popularity of Artificial Intelligence algorithms, machine learning and deep learning methods also begin to be applied in operation and maintenance scenarios, which is the emergence of Artificial Intelligence for IT Operations (AIOps), among which KPI anomaly detection is the research hotspot of AIOps. KPI anomaly detection refers to the effective data mining and analysis of KPI time series data through various anomaly detection methods, in order to quickly discover anomalies and avoid faults. Therefore, we propose a hybrid model of KPI anomaly detection based on GAN. This is an unsupervised learning method based on Generative adversarial network (GAN) and Encoder (Encoder), which can identify abnormal data in KPI. We train the GAN model with health data, and then complete the rapid mapping of new data to the potential space of the GAN model through the encoder, and then detect anomalies by combining the feature residuals of the discriminator and the data reconstruction error into an anomaly score. Experimental results show that, compared with the traditional KPI anomaly detection method and the general unsupervised method, the proposed method can capture the variable data characteristics of KPI more accurately and achieve better performance in the KPI anomaly detection task.
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