The aim of this paper is to illustrate the use of application and system level logs to better understand scientific data center behavior and energy-spending. Analyzing a data center log of 900 nodes (Sandy Bridge and Haswell), we study node power consumption and describe approaches to estimate and forecast it. Our results include methods to cluster nodes based on different vmstat and RAPL measurements as well as Gaussian and GAM models for estimating the plug power consumption. We also analyze failed jobs and find that non-successfully terminated jobs consume around 40% of computing time. While the actual numbers are likely to vary in different data centers at different times, the purpose of the paper is to share ideas of what can be found by statistical and machine learning analysis of large amount of log data.

本文的目的是说明如何使用应用程序和系统级日志来更好地了解数据中心的科学行为和能源消耗。通过分析 900 个节点（Sandy Bridge 和 Haswell）的数据中心日志，我们研究了节点功耗并描述了估计和预测它的方法。我们的结果包括基于不同 vmstat 和 RAPL 测量的节点集群方法以及用于估计插头功耗的高斯和 GAM 模型。我们还分析了失败的作业，发现未成功终止的作业消耗了大约 40% 的计算时间。虽然不同数据中心不同时间的实际数字可能会有所不同，但本文的目的是分享通过对大量日志数据进行统计和机器学习分析可以发现什么的想法。