Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services

• URL: http://arxiv.org/abs/2308.09937v1
• Date: Sat, 19 Aug 2023 08:08:05 GMT
• Title: Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services
• Authors: Jinyang Liu, Tianyi Yang, Zhuangbin Chen, Yuxin Su, Cong Feng, Zengyin Yang, Michael R. Lyu
• Abstract summary: CMAnomaly is an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of Huawei Cloud. Compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% to 10.68%, and runs 10X to 20X faster.
• Score: 29.37493773435177
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems' health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% to 10.68%, and runs 10X to 20X faster. Furthermore, we also share our experience of deploying CMAnomaly in Huawei Cloud.

Related papers

• ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper proposes a comprehensive visual anomaly detection benchmark, textbftextitADer, which is a modular framework for new anomaly detection methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• MELODY: Robust Semi-Supervised Hybrid Model for Entity-Level Online Anomaly Detection with Multivariate Time Series [11.754433499581879]
  A faulty code change may degrade the target service's performance and cause cascading outages in downstream services. In this paper, we study the problem of anomaly detection for deployments. We propose a novel framework, semi-supervised hybrid Model for Entity-Level Online Detection of anomalY (MELODY)
  arXiv  Detail & Related papers  (2024-01-18T19:02:41Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System [24.2074235652359]
  We propose MSTGAD, which seamlessly integrates all available data modalities via attentive multi-modal learning. We construct a transformer-based neural network with both spatial and temporal attention mechanisms to model the inter-correlations between different modalities. This enables us to detect anomalies automatically and accurately in real-time.
  arXiv  Detail & Related papers  (2023-10-07T06:28:41Z)
• Performance Issue Identification in Cloud Systems with Relational-Temporal Anomaly Detection [5.473091770227683]
  Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses. To ensure reliable performance, it's essential to accurately identify these issues using service monitoring metrics. Some existing methods tackle this problem by analyzing each metric independently to detect anomalies.
  arXiv  Detail & Related papers  (2023-07-20T13:41:26Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Federated Variational Learning for Anomaly Detection in Multivariate Time Series [13.328883578980237]
  We propose an unsupervised time series anomaly detection framework in a federated fashion. We leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models.
  arXiv  Detail & Related papers  (2021-08-18T22:23:15Z)
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals [10.866594993485226]
  We propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M) We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bigressive LSTM with Attention) to capture temporal dependence from time-series data.
  arXiv  Detail & Related papers  (2021-07-27T06:48:20Z)
• A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network [55.852401381113786]
  This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive-temporal quality framework for wireless access latency of connected vehicles. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Varienational Autocoder (VAE)
  arXiv  Detail & Related papers  (2020-03-16T03:43:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.