论文标题
云成分中的异常检测
Anomaly Detection in Cloud Components
论文作者
论文摘要
引擎盖下方的云平台由一个复杂的硬件和软件组件组成。这些组件中的每一个都可能失败，这可能导致中断。我们的目标是通过分析资源利用指标，通过早期发现此类故障来提高云服务的质量。我们测试了具有可能性功能的基于封闭式单元的自动编码器，以检测各种多维时间序列中的异常，并达到了高性能。
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.
