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| Hauptseite > Publikationsdatenbank > Towards Predictive Monitoring of Research Infrastructures > print |
| Hauptseite > Publikationsdatenbank > Towards Predictive Monitoring of Research Infrastructures > print |
| 001 | 862006 | ||
| 005 | 20210130001132.0 | ||
| 020 | _ | _ | |a 978-1-61208-700-9 |
| 037 | _ | _ | |a FZJ-2019-02380 |
| 100 | 1 | _ | |a Rybicki, Jedrzej |0 P:(DE-Juel1)144343 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a The Fifth International Conference on Big Data, Small Data, Linked Data and Open Data |g ALLDATA 2019 |c Valencia |d 2019-03-24 - 2019-03-28 |w Spain |
| 245 | _ | _ | |a Towards Predictive Monitoring of Research Infrastructures |
| 260 | _ | _ | |c 2019 |b IARIA |
| 295 | 1 | 0 | |a ALLDATA 2019, The Fifth International Conference on Big Data, Small Data, Linked Data and Open Data |
| 300 | _ | _ | |a 37-40 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1554290844_1356 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a Nowadays, both modern computing infrastructures, as well as their scientific workloads exhibit far reaching complexity and diversity. Therefore, it is increasingly hard to comprehend and manage them in an efficient manner. In particular, it can lead to under- or overuse of resources. A prerequisite for efficient resource allocation is the ability to predict their usage. In this paper, we use real world workloads recorded in a modern research infrastructure to conduct load prediction. We use well established statistical model (ARIMA) and achieve good results in predictions. The big data challenge is here given not by the sheer size but rather by the speed of data collection and processing. The quicker the prediction is made, the more time is available for actions. Such predictions can be included in monitoring systems to give human operators better insights into the status of their infrastructures and lead to better load distribution. |
| 536 | _ | _ | |a 512 - Data-Intensive Science and Federated Computing (POF3-512) |0 G:(DE-HGF)POF3-512 |c POF3-512 |f POF III |x 0 |
| 856 | 4 | _ | |u https://www.thinkmind.org/index.php?view=article&articleid=alldata_2019_5_10_80022 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/862006/files/alldata_2019_5_10_80022.pdf |y Restricted |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/862006/files/alldata_2019_5_10_80022.pdf?subformat=pdfa |x pdfa |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:862006 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)144343 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-512 |2 G:(DE-HGF)POF3-500 |v Data-Intensive Science and Federated Computing |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2019 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a contb |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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