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.

Academic Knowledge Services have substantially facilitated the development of human science and technology, providing a plenitude of useful research tools. However, many applications highly depend on ad-hoc models and expensive human labeling to understand professional contents, hindering deployments in real world. To create a unified backbone language model for various knowledge-intensive academic knowledge mining challenges, based on the world's largest public academic graph Open Academic Graph (OAG), we pre-train an academic language model, namely OAG-BERT, to integrate massive heterogeneous entity knowledge beyond scientific corpora. We develop novel pre-training strategies along with zero-shot inference techniques. OAG-BERT's superior performance on 9 knowledge-intensive academic tasks (including 2 demo applications) demonstrates its qualification to serve as a foundation for academic knowledge services. Its zero-shot capability also offers great potential to mitigate the need of costly annotations. OAG-BERT has been deployed to multiple real-world applications, such as reviewer recommendations for NSFC (National Nature Science Foundation of China) and paper tagging in the AMiner system. All codes and pre-trained models are available via the CogDL.