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Monitoring distributed computing beyond the traditional time-series histogram
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| Zeitschriftentitel: | EPJ Web of Conferences |
|---|---|
| Personen und Körperschaften: | , , |
| In: | EPJ Web of Conferences, 245, 2020, S. 03036 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
EDP Sciences
|
| Schlagwörter: |
| author_facet |
Doidge, M S Love, P. A. Thornton, J Doidge, M S Love, P. A. Thornton, J |
|---|---|
| author |
Doidge, M S Love, P. A. Thornton, J |
| spellingShingle |
Doidge, M S Love, P. A. Thornton, J EPJ Web of Conferences Monitoring distributed computing beyond the traditional time-series histogram General Earth and Planetary Sciences General Engineering General Environmental Science |
| author_sort |
doidge, m s |
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Doidge, M S Love, P. A. Thornton, J 2100-014X EDP Sciences General Earth and Planetary Sciences General Engineering General Environmental Science http://dx.doi.org/10.1051/epjconf/202024503036 <jats:p>In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.</jats:p> Monitoring distributed computing beyond the traditional time-series histogram EPJ Web of Conferences |
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| title |
Monitoring distributed computing beyond the traditional time-series histogram |
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Monitoring distributed computing beyond the traditional time-series histogram |
| title_full |
Monitoring distributed computing beyond the traditional time-series histogram |
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Monitoring distributed computing beyond the traditional time-series histogram |
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Monitoring distributed computing beyond the traditional time-series histogram |
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Monitoring distributed computing beyond the traditional time-series histogram |
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monitoring distributed computing beyond the traditional time-series histogram |
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General Earth and Planetary Sciences General Engineering General Environmental Science |
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http://dx.doi.org/10.1051/epjconf/202024503036 |
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2020 |
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<jats:p>In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.</jats:p> |
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| description | <jats:p>In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.</jats:p> |
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| spelling | Doidge, M S Love, P. A. Thornton, J 2100-014X EDP Sciences General Earth and Planetary Sciences General Engineering General Environmental Science http://dx.doi.org/10.1051/epjconf/202024503036 <jats:p>In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.</jats:p> Monitoring distributed computing beyond the traditional time-series histogram EPJ Web of Conferences |
| spellingShingle | Doidge, M S, Love, P. A., Thornton, J, EPJ Web of Conferences, Monitoring distributed computing beyond the traditional time-series histogram, General Earth and Planetary Sciences, General Engineering, General Environmental Science |
| title | Monitoring distributed computing beyond the traditional time-series histogram |
| title_full | Monitoring distributed computing beyond the traditional time-series histogram |
| title_fullStr | Monitoring distributed computing beyond the traditional time-series histogram |
| title_full_unstemmed | Monitoring distributed computing beyond the traditional time-series histogram |
| title_short | Monitoring distributed computing beyond the traditional time-series histogram |
| title_sort | monitoring distributed computing beyond the traditional time-series histogram |
| title_unstemmed | Monitoring distributed computing beyond the traditional time-series histogram |
| topic | General Earth and Planetary Sciences, General Engineering, General Environmental Science |
| url | http://dx.doi.org/10.1051/epjconf/202024503036 |