面向混部云失败批处理作业的预测算法

为了降低混部云失败批处理作业的风险,使用K-means聚类算法将批处理作业分为四类,在分类的基础上提出了二层嵌套分类模型(two-layer nested classification model, TLNM),实现了基于TLNM的预测算法。基于Ali Trace 2018数据集上的实验结果表明,该算法的接受者操作特性(receiver operating characteristic, ROC)曲线明显优于其他常用分类器,ROC曲线下面积(即AUC)可以达到0.978,表明该算法具有良好的分类性能。同时召回率可以达到0.951,通过混淆矩阵可以看出TLNM算法能够准确预测出执行失败的批处理作业。

Prediction algorithm for failed batch jobs in co-located cloud

In order to reduce the risk of failed batch jobs in co-located cloud, the K-means algorithm was used to divide batch jobs into four categories.On the basis of classification, the TLNM (two-layer nested classification model) was proposed and the prediction algorithm based on TLNM was implemented. Experiment results based on Ali Trace 2018 data set show that the ROC(receiver operating characteristic) curve of this algorithm is significantly better than other commonly used classifiers, and the area under the ROC curve (i.e.AUC) can reach 0.978, indicating that this algorithm has good classification performance. At the same time, the recall rate can reach 0.951. Through the confusion matrix, it can be seen that the TLNM algorithm can accurately predict the failed batch jobs.