IT services are now popularly hosted in cloud systems. In order to enhance the availability of cloud services, an emerging approach for detecting failures of cloud components is to monitor Key Performance Indicators (KPIs) of the components and apply Neural Network based AI technologies to detect KPI anomalies. Multivariate Time Series Anomaly Detection (TSAD) models have been designed for this purpose. However, when applying such models directly to real-world cloud systems the anomaly detection performance is not as good. This is because the number of KPIs in real cloud systems is typically much more than the number of KPIs in the datasets used for model evaluation, and the larger number of KPIs bring about a performance loss of the models’ anomaly detection. Therefore, selecting KPIs properly is essential for applying multivariant KPI data for any practical anomaly detection. This paper studies this performance loss issue when TSAD models are applied onto real-world cloud systems, and proposes fKPISelect, a mechanism of automated KPI selection based on fault injection. We implemented fKPISelect, deployed it to a real cloud system, and created a real-world KPI dataset. We conducted extensive experiments, and the experimental results show the effectiveness and practicality of fKPISelect: it improves the F1 score of anomaly detection from 0.68 to 0.91 for real-world KPI data.

The Internet services generate huge amount of data, which require large space for storage. Determining device purchase plan turns out to be very important for the service providers. Under-purchasing might lead to data loss, while over-purchasing would result in waste. In this paper, we propose a linear regression based approach to predict the storage demand according to the time series of the storage consumption. We partitioned the storage con-sumption time series into several linear segments, and perform prediction on the last segment using linear regression. Since the position of turning points between adjacent segments and the total number of the segments are both unknown, how to achieve the online segmentation becomes a big challenge. Aiming to solve this problem, we carried out the Kalman-Anova segmentation method. Experiment results show that our method has good accuracy in precision, recall and F-measure values. Moreover, the method is able to segment nonlinear time series as well, suggesting a potential wider application. The proposed method has been deployed in Baidu Inc. and saves about 45 thousand dollars in one of its device purchase program.