  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 algorithm is
developed to identify anomalies based on the specific temporal patterns of the
given metrics data (e.g., the periodic pattern), the results of which are
visualized in our system to indicate the occurrences of anomalies. Rich
visualization and interaction designs are used to help understand the anomalies
in the spatial and temporal context. We demonstrate the effectiveness of
CloudDet through a quantitative evaluation, two case studies with real-world
data, and interviews with domain experts.
