演化粒度分类器在计算中心预测维护异常检测中的应用比较
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers
摘要：
计算中心基于日志的预测性维护是支持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。日志作为面向事件的临时信息，通常以非结构化大数据的形式给出。测井数据处理是一项耗时的计算任务。目标是从不断变化的网格环境中获取必要的信息，以构建分类模型。不断发展的粒度分类器适合于从随时间变化的日志流中学习，因此可以对异常的严重程度进行在线分类。我们制定了一个4类在线异常分类问题，并采用时间窗口之间的地标和两种粒度计算方法，即基于模糊集的进化建模（FBeM）和进化粒度神经网络（eGNN），建模和监测测井活动率。分类结果对于预测性维护至关重要，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。
英文摘要：
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.
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