This paper proposes a novel non-intrusive system failure prediction technique using available information from developers and minimal information from raw logs (rather than mining entire logs) but keeping the data entirely private with the data owners. A neural network based multi-class classifier is developed for failure prediction, using artificially generated anonymous data set, applying a combination of techniques, viz., genetic algorithm (steps), pattern repetition, etc., to train and test the network. The proposed mechanism completely decouples the data set used for training process from the actual data which is kept private. Moreover, multi-criteria decision making (MCDM) schemes are used to prioritize failures meeting business requirements. Results show high accuracy in failure prediction under different parameter configurations. On a broader context, any classification problem, beyond failure prediction, can be performed using the proposed mechanism with artificially generated data set without looking into the actual data as long as the input features can be translated to binary values (e.g. output from private binary classifiers) and can provide classification-as-a-service.
翻译:本文建议采用新的非侵入性系统故障预测技术,使用开发者提供的信息和原始原木(而不是开采整个原木)的最低限度信息,但将数据完全与数据所有者保持保密。 开发了以神经网络为基础的多级分类器,用于故障预测,使用人工生成的匿名数据集,采用各种技术组合,即遗传算法(步骤)、模式重复等,来培训和测试网络。 拟议的机制将用于培训过程的数据集与实际的保密数据完全分离。 此外,多标准决策(MCDM)方案用于确定符合业务要求的故障的优先顺序。结果显示,在不同参数配置下,故障预测的准确度很高。 在更广泛的背景下,任何分类问题,除了故障预测之外,可以使用人工生成的数据集来进行,只要输入特征可以转化为二元值(例如私人二进制分类器的产出),并且能够提供分类服务。