Dataset Open Access

Antarex HPC Fault Dataset

Alessio Netti; Zeynep Kiziltan; Ozalp Babaoglu; Alina Sirbu; Andrea Bartolini; Andrea Borghesi


MARC21 XML Export

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    <subfield code="a">The archive contains 4 directories, one for each block of the dataset - namely CPU/Memory and HDD, in single-core and multi-core variants. In each of these directories, you will find the following: a 7z archive containing the LDMS CSV files for each of the 7 used plugins; FINJ workloads and execution logs; the histograms for the durations and inter-arrival times of fault tasks in PDF format; launch scripts, if any. Source code for all of the injected fault programs and additional details can be found on the GitHub repository of the FINJ tool.</subfield>
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    <subfield code="a">&lt;p&gt;The Antarex dataset contains trace data collected from the&amp;nbsp;homonymous experimental HPC system located at ETH Zurich&amp;nbsp;while it was subjected to fault injection,&amp;nbsp;for the purpose of conducting machine learning-based fault detection studies for&amp;nbsp;HPC systems. Acquiring our own dataset was made necessary&amp;nbsp;by the fact that commercial HPC system operators are very&amp;nbsp;reluctant to share trace data containing information about&amp;nbsp;faults in their systems.&lt;/p&gt;

&lt;p&gt;In order to acquire data, we executed benchmark applications and at the same time injected faults in the system&amp;nbsp;at specific times via dedicated programs, so as to trigger&amp;nbsp;anomalies in the behaviour of the applications. A wide range of faults is covered in our dataset, from hardware faults, to misconfiguration faults, and finally to performance anomalies cause by interference from other processes.&amp;nbsp;This was achieved through the FINJ fault injection tool, developed by the authors.&lt;/p&gt;

&lt;p&gt;The dataset contains two types of data: one type of&amp;nbsp;data&amp;nbsp;refers to a series of CSV files, each containing a set of system performance metrics sampled through the LDMS&amp;nbsp;HPC monitoring framework. Another type refers to the log&amp;nbsp;files detailing the status of the system (i.e., currently running&amp;nbsp;benchmark applications or injected fault programs) at each&amp;nbsp;time point in the dataset. Such a structure enables researchers&amp;nbsp;to perform a wide range of studies on the dataset. Moreover,&amp;nbsp;since we collected the dataset by streaming continuous data,&amp;nbsp;any study based on it will easily be reproducible on a real&amp;nbsp;HPC system, in an online way.&amp;nbsp;The dataset is divided in two parts:&amp;nbsp;the first&amp;nbsp;includes&amp;nbsp;only the CPU and memory-related benchmark applications&amp;nbsp;and fault programs, while the second is strictly hard drive-related. We executed each part in both single-core and multi-core variants, resulting in a total of 4 dataset blocks for 32 days of data acquisition, and 20GB of uncompressed data.&lt;/p&gt;

&lt;p&gt;For a detailed analysis on&amp;nbsp;the structure and features of the Antarex dataset, please refer to the&amp;nbsp;research paper&amp;nbsp;&amp;quot;Online Fault Classification in HPC System through Machine Learning&amp;quot;, by Netti et al. Additional details can be found in the research paper &amp;quot;FINJ: a Fault Injection Tool for HPC System&amp;quot; by Netti et al., whereas all source code can be found on the GitHub repository of the FINJ tool.&lt;/p&gt;

&lt;p&gt;When using this dataset, please cite the two reference papers above as follows:&lt;/p&gt;

&lt;p&gt;&amp;quot;&amp;nbsp;Netti A., Kiziltan Z., Babaoglu O., S&amp;icirc;rbu A., Bartolini A., Borghesi A. (2019) FINJ: A Fault Injection Tool for HPC Systems. In: Mencagli G. et al. (eds) Euro-Par 2018: Parallel Processing Workshops. Euro-Par 2018. Lecture Notes in Computer Science, vol 11339. Springer, Cham&amp;quot;&lt;/p&gt;

&lt;p&gt;&amp;quot; Netti A., Kiziltan Z., Babaoglu O., S&amp;icirc;rbu A., Bartolini A., Borghesi A. (2019) Online Fault Classification in HPC Systems through Machine Learning. arXiv:1810.11208&amp;quot;&lt;/p&gt;</subfield>
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