CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems
Ke Xu, Yun Wang, Leni Yang, Yifang Wang, Bo Qiao, Si Qin, Yong Xu,, Haidong Zhang, Huamin Qu

TL;DR
CloudDet is an interactive visual analytics system designed to detect, inspect, and diagnose anomalies in cloud computing performance data, combining a novel unsupervised detection algorithm with rich visualizations for better understanding.
Contribution
The paper introduces CloudDet, a unified visual analytics system with a new unsupervised anomaly detection algorithm tailored for cloud performance data analysis.
Findings
Effective detection of anomalies in cloud systems.
Rich visualizations aid in understanding anomalies.
System validated through case studies and expert interviews.
Abstract
Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been developed to identify anomalies in cloud computing performance. These techniques are usually adopted to track the performance metrics of the system (e.g., CPU, memory, and disk I/O), represented by a multivariate time series. However, given the complex characteristics of cloud computing data, the effectiveness of these automated methods is affected. Thus, substantial human judgment on the automated analysis results is required for anomaly interpretation. In this paper, we present a unified visual analytics system named CloudDet to interactively detect, inspect, and diagnose anomalies in cloud computing systems. A novel unsupervised anomaly detection…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Data Visualization and Analytics · Time Series Analysis and Forecasting
