大规模的假名化：OLCF的Summit使用数据案例研究
Pseudonymization at Scale: OLCF's Summit Usage Data Case Study
摘要：
大量数据的分析和复杂计算作业的处理传统上依赖于高性能计算（HPC）系统。了解这些分析的需求对于设计能够带来更好科学的解决方案至关重要，同样，了解这些系统上的用户行为特征对于改善HPC系统上的用户体验也很重要。收集有关用户行为的数据的常用方法是分析仅系统管理员可用的系统日志数据。然而，最近在橡树岭领导计算设施（OLCF），我们通过使用普通Unix命令从用户的角度收集数据，揭示了Summit超级计算机的用户行为。在这里，我们讨论了准备发布和提交开放数据挑战的过程，挑战和经验教训。原始数据集包含有关OLCF用户的个人身份信息（PII），这些信息需要在发布之前进行屏蔽，我们确定完全擦洗PII的匿名化破坏了太多的数据结构，因此无法进行数据挑战。相反，我们选择对数据集进行匿名化，以减少其与用户身份的关联性。假名化比匿名化在计算上要昂贵得多，我们的数据集的大小（大约1.75亿行原始文本）需要开发一个可以在不同HPC机器上重用的并行工作流。我们在OLCF的两个领导级HPC系统上演示了工作流的扩展行为，并且我们表明，我们能够将单个节点上的整体完工时间从不切实际的20多个小时降低到2小时左右。作为这项工作的结果，我们发布了整个匿名数据集，并公开了工作流程和源代码。
英文摘要：
The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems. Understanding these analyses' needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to analyze system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user's point of view with ordinary Unix commands. Here, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about OLCF users which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset to reduce its linkability to users' identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.
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