Correlation-wise Smoothing：面向HPC监测数据的轻量级知识抽取
Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data
摘要：
现代高性能计算（HPC）和数据中心运营商越来越依赖数据分析技术来提高其运营的效率和可靠性。它们采用的模型可以摄取时间序列监测传感器数据，并将其转换为可操作的知识用于系统调优：这一过程称为操作数据分析（ODA）。然而，监测数据具有高维度，依赖于硬件并且难以解释。这一点，加上ODA的严格要求，使得大多数传统的数据采掘方法不切实际，反过来又使得这种类型的数据处理繁琐。大多数当前的ODA解决方案使用非通用的ad-hoc处理方法，对传感器的特征敏感并且不适合可视化。在本文中，我们提出了一种新的方法，称为相关平滑（CS），提取描述性签名的时间序列监测数据中的通用和轻量级的方式。我们的CS方法利用数据维度之间的相关性来形成组，并产生可以轻松操作，可视化和比较的图像状签名。我们在HPC-ODA上评估了CS方法，HPC-ODA是我们与这项工作一起发布的数据集集合，并表明它与大多数最先进的方法具有相同的性能，同时产生的签名最多可以小十倍，速度最多可以快十倍，同时获得可视化，跨系统的可移植性和清晰的缩放特性。
英文摘要：
Modern High-Performance Computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations. They employ models that ingest time-series monitoring sensor data and transform it into actionable knowledge for system tuning: a process known as Operational Data Analytics (ODA). However, monitoring data has a high dimensionality, is hardware-dependent and difficult to interpret. This, coupled with the strict requirements of ODA, makes most traditional data mining methods impractical and in turn renders this type of data cumbersome to process. Most current ODA solutions use ad-hoc processing methods that are not generic, are sensible to the sensors' features and are not fit for visualization. In this paper we propose a novel method, called Correlation-wise Smoothing (CS), to extract descriptive signatures from time-series monitoring data in a generic and lightweight way. Our CS method exploits correlations between data dimensions to form groups and produces image-like signatures that can be easily manipulated, visualized and compared. We evaluate the CS method on HPC-ODA, a collection of datasets that we release with this work, and show that it leads to the same performance as most state-of-the-art methods while producing signatures that are up to ten times smaller and up to ten times faster, while gaining visualizability, portability across systems and clear scaling properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
