Practical data monitoring in the internet-services domain
Large-scale monitoring, anomaly detection, and root cause analysis of metrics are essential requirements of the internet-services industry. To address the need to continuously monitor millions of metrics, many anomaly detection approaches are being used on a daily basis by large internet-based companies. However, in spite of the significant progress made to accurately and efficiently detect anomalies in metrics, the sheer scale of the number of metrics has meant there are still a large number of false alarms that need to be investigated. This paper presents a framework for reliable large-scale anomaly detection. It is significantly more accurate than existing approaches and allows for easy interpretation of models, thus enabling practical data monitoring in the internet-services domain.
Code (1)
Tasks
Anomaly DetectionSimilar Papers 제목 키워드 기반
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series
We propose a Web-Mashup Application Service Framework for Multivariate Time Series Analytics (MTSA) that supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decis…
parameter estimationTime SeriesTime Series AnalysisFederated Kalman Filter for Secure IoT-based Device Monitoring Services
Device monitoring services have increased in popularity with the evolution of recent technology and the continuously increased number of Internet of Things (IoT) devices. Among the popular services are the ones that use …
Federated LearningPredicting IPv4 Services Across All Ports
Internet-wide scanning is commonly used to understand the topology and security of the Internet. However, IPv4 Internet scans have been limited to scanning only a subset of services -- exhaustively scanning all IPv4 serv…
AllDriving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the…
Cloud ComputingDistributed ComputingEdge-computingFault DetectionTransforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into…
Medical DiagnosisTime SeriesTime Series Analysis