Title
Monitoring parallel file system usage in a high-performance computer cluster
Author
Jaan Tollander de Balsch
Supervisor
Prof. Petteri Kaski
Advisor
Dr. Sami Ilvonen
Degreeprogram
Computer, Communication and Information Sciences
Major
Computer Science
Keywords
monitoring computer systems, observability, computer cluster, high-performance computing, parallel file system, Lustre, I/O behavior, time series analysis, exploratory data analysis
License
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
URN
http://urn.fi/URN:NBN:fi:aalto-202303262552
Many high-performance computer clusters, rely on a system-wide, shared, parallel file system for large storage capacity and bandwidth. A shared file system is available across the entire system, making it user-friendly but prone to problems from heavy use. Such use can cause congestion and slow down or even halt the whole system, harming all users who use the parallel file system. In this thesis, we investigate whether monitoring file system usage in a production system at CSC can help identify the causes of slowdowns, such as specific users or jobs. The long-goal at CSC is to build an automatic, real-time monitoring and warning system that system administrators can use to make decisions on alleviating the slowdowns. Specifically, we monitor the usage of the Lustre parallel file system with Lustre Jobstats feature in the Puhti cluster, which is a petascale cluster with a diverse user base. We explain the necessary details of the Puhti cluster and our monitoring system to understand the Lustre file system usage data. During the thesis, we discovered issues in the data quality from Lustre Jobstats. The issues affected identifiers in the data, making some data unreliable and limiting our ability to build an automatic, real-time analysis. Nevertheless, we obtained a feasible data set for explorative data analysis. We demonstrate 24 hours of monitoring data by visually demonstrating file system usage patterns at low and high-level. Furthermore, we show that we can use file system usage data to identify causes of relative changes in I/O trends, particularly large relative increases. Finally, we explore ideas for future work on monitoring file system usage with reliable data from longer periods.
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