تشخیص ناهنجاری گره برای محیط های توزیع شده همگن

To address these issues, in this paper, we introduce a new type of anomalies called contextual collective anomaly. Then we propose a framework to discover this type of anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can accurately identify the anomalies by taking both the contextual information and the historical information of a data stream into consideration. Also, the proposed framework is designed in a way with a low computational cost, and is able to handle large-scale data streams. To demonstrate the effectiveness and efficiency of our proposed framework, we empirically validate it on a real world cluster.