A Fast High-Dimensional Tool for Detecting Anomalistic Nodes in Large Scale Systems (LSAND)

Today, large scale computer systems have become an important component in production and scientific computing and lead to rapid advances in many disciplines. However, the size, and complexity of systems make them very difficult to detect unusual nodes automatically and traditional host monitoring tools are not capable of dealing with the need of anomaly detection in large amount of nodes. In this paper, we introduce a novel tool, LSAND, which could detect anomalistic nodes in a horizontal view of the machines with comparable configuration and tasks running on. We evaluated LSAND in a cluster environment, table the results of our experiment and give a discussion on the effect and show that LSAND is both effective and efficient for detecting anomalistic nodes with high-dimensional features.