Anomaly Detection in Cloud Components
This addresses the problem of improving cloud service quality through early failure detection for cloud platform operators, but it appears incremental as it applies an existing method to a specific domain.
The paper tackled the problem of detecting failures in cloud platforms by analyzing resource utilization metrics, achieving high performance with a Gated-Recurrent-Unit-based autoencoder and likelihood function for anomaly detection in multi-dimensional time series.
Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multi-dimensional time series and achieved high performance.