Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale software systems. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve optimal performance, which often conflicts with the frequent pattern changes observed in software systems. Moreover, the training overhead of vast MTS in large-scale software systems is unacceptably high. To address these issues, we design OmniTransfer , a model-agnostic framework that combines weighted hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large web content service provider and a network operator show that OmniTransfer significantly reduces the model initialization time by 46.49% and the training cost by 74.51%, while maintaining high accuracy in detecting anomalies.

Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale Web services. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve good performance, which conflicts with frequent pattern changes in Web services entities. Moreover, the training overhead of vast MTS in large-scale Web services is unacceptable. To address these issues, we design OmniTransfer, a model-agnostic framework that combines improved hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large Web content service provider show that OmniTransfer significantly reduces the model initialization time by 59.72% and the training cost by 85.01%, while maintaining high accuracy in detecting anomalies.

Using multivariate time series (MTS) data for anomaly detection is widely adopted in service systems, such as web services and financial businesses. Researchers have recently proposed some well-performed algorithms for MTS anomaly detection from different perspectives. When applied to the real world, we observe that none of the algorithms is adaptable to all scenarios due to the complex data and anomaly characteristics. Moreover, there is currently a lack of comprehensive analysis work of these algorithms to guide operators in selecting the appropriate one in practice. To bridge this gap, we conduct an empirical study using various real-world data to gain an in-depth understanding of state-of-the-art anomaly detection algorithms. First, we provide general recommendations to guide operators in selecting suitable models based on the volume of training data, computational resources, and effectiveness requirements. Then, we summarize the typical data characteristics and types of anomalies and offer tailored model selection suggestions for different data characteristics and anomaly types. At last, we apply the summarized model selection suggestions to all the datasets we collected. The results show that most of our suggestions can achieve better than any single algorithm alone, demonstrating the effectiveness and generalization of our recommendations.

To ensure the performance of large-scale datacenters, operators need to monitor up to tens of millions of various-type KPIs, e.g., CPU utilization, memory utilization. For each KPI, it is crucial but challenging to detect outliers that deviate from its historical patterns or the patterns of other KPIs in the same period. In this work, we proposeOutSpot, an unsupervised outlier detection framework that integrates hierarchical agglomerative clustering (HAC) with conditional variational autoencoder (CVAE), which significantly improves computational efficiency and comprehensively learns the above two patterns. Additionally, two simple yet effective techniques, soft threshold and median filter, are applied to precisely determine outlier KPIs. Using two real-world datasets collected from the datacenters owned by a top-tier global short video service provider and a top-tier domestic operator,respectively. It demonstrates thatOutSpotachieves the best F1 score of 0.95 and 0.91, AUC of 0.99 and 0.99 on the two datasets, significantly outperforming seven baseline outlier detection methods.

Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider$H$with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.

System instance clustering is crucial for large-scale Web services because it can significantly reduce the training overhead of anomaly detection methods. However, the vast number of system instances with massive time points, redundant metrics, and noise bring significant challenges. We propose OmniCluster to accurately and efficiently cluster system instances for large-scale Web services. It combines a one-dimensional convolutional autoencoder (1D-CAE), which extracts the main features of system instances, with a simple, novel, yet effective three-step feature selection strategy. We evaluated OmniCluster using real-world data collected from a top-tier content service provider providing services for one billion+ monthly active users (MAU), proving that OmniCluster achieves high accuracy (NMI=0.9160) and reduces the training overhead of five anomaly detection models by 95.01% on average.