Analysis and Workload Characterization of the CERN EOS Storage System

Q3 Computer Science

Operating Systems Review (ACM) Pub Date : 2022-06-14 DOI:10.1145/3544497.3544507

Devashish R. Purandare, Daniel Bittman, E. L. Miller

引用次数: 0

Abstract

Modern, large-scale scientific computing runs on complex exascale storage systems that support even more complex data workloads. Understanding the data access and movement patterns is vital for informing the design of future iterations of existing systems and next-generation systems. Yet we are lacking in publicly available traces and tools to help us understand even one system in depth, let alone correlate long-term cross-system trends.

查看原文本刊更多论文

CERN EOS存储系统的分析与工作负载表征

现代的大规模科学计算运行在复杂的百亿亿级存储系统上，这些系统支持更复杂的数据工作负载。理解数据访问和移动模式对于告知现有系统和下一代系统的未来迭代设计至关重要。然而，我们缺乏公开可用的跟踪和工具来帮助我们深入了解一个系统，更不用说关联长期的跨系统趋势了。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Operating Systems Review (ACM) Computer Science-Computer Networks and Communications

CiteScore

2.80

自引率

0.00%

发文量

期刊介绍： Operating Systems Review (OSR) is a publication of the ACM Special Interest Group on Operating Systems (SIGOPS), whose scope of interest includes: computer operating systems and architecture for multiprogramming, multiprocessing, and time sharing; resource management; evaluation and simulation; reliability, integrity, and security of data; communications among computing processors; and computer system modeling and analysis.