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dc.contributor.authorChircu, Alina
dc.contributor.authorSultanow, Eldar
dc.contributor.authorBaum, David
dc.contributor.authorKoch, Christian
dc.contributor.authorSeßler, Matthias
dc.contributor.editorDraude, Claude
dc.contributor.editorLange, Martin
dc.contributor.editorSick, Bernhard
dc.date.accessioned2019-08-27T13:00:16Z
dc.date.available2019-08-27T13:00:16Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-689-3
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/25052
dc.description.abstractIn 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.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-295
dc.subjectData Center
dc.subjectForecasting
dc.subjectMachine Learning
dc.subjectMonitoring
dc.subjectResponse Time
dc.subjectTime Series
dc.subjectUtilization
dc.subjectVisualization
dc.titleVisualization and Machine Learning for Data Center Managementen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages23-35
mci.conference.sessiontitleEnterprise Architecture Management in Forschung und Praxis
mci.conference.locationKassel
mci.conference.date23.-26. September 2019
dc.identifier.doi10.18420/inf2019_ws02


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