论文标题
将不断发展的颗粒分类器应用于计算中心中预测维护的颗粒分类器的比较
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers
论文作者
论文摘要
基于日志的计算中心的预测维护是关于支持CERN（欧洲核研究组织）物理实验的全球计算网格的主要关注点。作为面向事件的Adhoc信息，日志通常被称为非结构化的大数据。日志数据处理是一项耗时的计算任务。目的是从不断变化的网格环境中获取基本信息来构建分类模型。不断发展的粒状分类器适合从随着时变的日志流中学习，因此对异常的严重程度进行在线分类。我们提出了一个4级的在线异常分类问题，并采用了地标之间的时间窗口和两种颗粒状计算方法，即基于模糊的基于模糊的演变建模（FBEM）和不断发展的颗粒神经网络（EGNN），以建模和监视记录活动率。分类结果对于预测维护至关重要，因为可以优先考虑特定的时间间隔，其中分类器指示存在高或中等严重性异常。
Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented adhoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task. The goal is to grab essential information from a continuously changeable grid environment to construct a classification model. Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classification of the severity of anomalies. We formulated a 4-class online anomaly classification problem, and employed time windows between landmarks and two granular computing methods, namely, Fuzzy-set-Based evolving Modeling (FBeM) and evolving Granular Neural Network (eGNN), to model and monitor logging activity rate. The results of classification are of utmost importance for predictive maintenance because priority can be given to specific time intervals in which the classifier indicates the existence of high or medium severity anomalies.
