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.

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.

Timely anomaly detection of key performance indicators (KPIs),e.g., service response time, error rate, is of utmost importance to Web services. Over the years, many unsupervised deep learning-based anomaly detection approaches have been proposed. To achieve good performance, they require a long period of KPI data for model training, which is not easy to guarantee with frequent service changes. Additionally, the training overhead is too significant for the vast number of KPIs in large-scale Web services. To address the problems, we propose an unsupervised KPI anomaly detection approach, namedAnoTransfer, by combining a novel Variational Auto-Encoder (VAE)-based KPI clustering algorithm with an adaptive transfer learning strategy. Extensive evaluation experiments using real-world data collected from several large-scale Web service providers demonstrate thatAnoTransferreduces the average initialization time by 65.71% and improves the training efficiency by 50.62 times, without significantly degrading anomaly detection accuracy.

System logs, which describe a variety of events of software systems, are becoming increasingly popular for anomaly detection. However, for a large software system, current unsupervised learning-based methods are suffering from low accuracy due to the high diversity of logs, while the supervised learning methods are nearly infeasible to be used in practice because it is time-consuming and labor-intensive to obtain sufficient labels for different types of software systems. In this paper, we propose a novel framework, LogTransfer, which applies transfer learning to transfer the anomalous knowledge of one type of software system (source system) to another (target system). We represent every template using Glove, which considers both global word co-occurrence and local context information, to address the challenge that different types of software systems are different in log syntax while the semantics of logs should be reserved. We apply an LSTM network to extract the sequential patterns of logs, and propose a novel transfer learning method sharing fully connected networks between source and target systems, to minimize the impact of noises in anomalous log sequences. Extensive experiments have been performed on switch logs of different vendors collected from a top global cloud service provider. LogTransfer achieves an averaged 0.84 F1-score and outperforms the state-of-the-art supervised and unsupervised log-based anomaly detection methods, which are consistent with the experiments conducted on the public HDFS and Hadoop application datasets.