论文标题
数据中心中的实时异常检测，用于基于日志的预测维护，使用基于模糊规则的方法
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach
论文作者
论文摘要
数据中心中异常行为的检测对于预测维护和数据安全至关重要。对于数据中心，我们是指允许用户传输和交换数据和信息的任何计算机网络。特别是，我们专注于意大利核物理学研究所（INFN）的Tier-1数据中心，该研究所支持日内瓦大型强子对撞机（LHC）的高能物理实验。该中心提供数据处理，存储，分析和分发所需的资源和服务。数据中心的日志记录本质上是一种随机和非平稳现象。我们提出了一种实时方法，以基于滑动时间窗口监视和分类日志记录，以及一个随时间变化的基于模糊规则的分类模型。根据控制图最常见的日志模式被视为正常系统状态。我们从时间窗口中提取属性，以逐渐开发和更新不断发展的高斯模糊分类器（EGFC）。实时异常监测系统必须在准确性，紧凑和实时操作方面提供令人鼓舞的结果。
Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy physics experiments at the Large Hadron Collider (LHC) in Geneva. The center provides resources and services needed for data processing, storage, analysis, and distribution. Log records in the data center is a stochastic and non-stationary phenomenon in nature. We propose a real-time approach to monitor and classify log records based on sliding time windows, and a time-varying evolving fuzzy-rule-based classification model. The most frequent log pattern according to a control chart is taken as the normal system status. We extract attributes from time windows to gradually develop and update an evolving Gaussian Fuzzy Classifier (eGFC) on the fly. The real-time anomaly monitoring system has to provide encouraging results in terms of accuracy, compactness, and real-time operation.
