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 highdimensional features.
anomaly detection, large scale system2 highdimension data set 3 approximate nearest neighbor4
Ying Zhao Gang Shao Guangwen Yang
Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University, Beijing 100084, China
国际会议
重庆
英文
211-216
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）