Visualization and Machine Learning for Data Center Management
| dc.contributor.author | Chircu, Alina | |
| dc.contributor.author | Sultanow, Eldar | |
| dc.contributor.author | Baum, David | |
| dc.contributor.author | Koch, Christian | |
| dc.contributor.author | Seßler, Matthias | |
| dc.contributor.editor | Draude, Claude | |
| dc.contributor.editor | Lange, Martin | |
| dc.contributor.editor | Sick, Bernhard | |
| dc.date.accessioned | 2019-08-27T13:00:16Z | |
| dc.date.available | 2019-08-27T13:00:16Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In this paper, we present a novel tool for data center management that incorporates data visualization and machine learning capabilities. We developed the tool in the context of an action design research project conducted at a large government agency in Germany, which hosts three highly available data centers containing more than 10,000 servers. We derived the requirements for the tool from qualitative interviews with agency employees who are familiar with monitoring the data center infrastructure as well as from a review of existing data center and other large infrastructure monitoring solutions. We implemented a web-based 3D prototype for the tool as an Angular 6 application running on Node.js, and evaluated it with the same employees. Most participants preferred the new tool, which provided a significantly better option and enabled visualization of historical data for all server instances at the same time, as well as real-time charts. Planned improvements will take advantage of the full potential of machine learning for time series forecasting. | en |
| dc.identifier.doi | 10.18420/inf2019_ws02 | |
| dc.identifier.isbn | 978-3-88579-689-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/25052 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge) | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-295 | |
| dc.subject | Data Center | |
| dc.subject | Forecasting | |
| dc.subject | Machine Learning | |
| dc.subject | Monitoring | |
| dc.subject | Response Time | |
| dc.subject | Time Series | |
| dc.subject | Utilization | |
| dc.subject | Visualization | |
| dc.title | Visualization and Machine Learning for Data Center Management | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 35 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 23 | |
| gi.conference.date | 23.-26. September 2019 | |
| gi.conference.location | Kassel | |
| gi.conference.sessiontitle | Enterprise Architecture Management in Forschung und Praxis |
Dateien
Originalbündel
1 - 1 von 1