Rabl, Tilmann ; Frank, Michael ; Sergieh, Hatem Mousselly ; Kosch, Harald (2011)
A Data Generator for Cloud-scale Benchmarking.
Singapore
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In many fields of research and business data sizes are breaking the petabyte barrier. This imposes new problems and research possibilities for the database community. Usually, data of this size is stored in large clusters or clouds. Although clouds have become very popular in recent years, there is only little work on benchmarking cloud applications. In this paper we present a data generator for cloud sized applications. Its architecture makes the data generator easy to extend and to configure. A key feature is the high degree of parallelism that allows linear scaling for arbitrary numbers of nodes. We show how distributions, relationships and dependencies in data can be computed in parallel with linear speed up.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | Rabl, Tilmann ; Frank, Michael ; Sergieh, Hatem Mousselly ; Kosch, Harald |
| Art des Eintrags: | Bibliographie |
| Titel: | A Data Generator for Cloud-scale Benchmarking |
| Sprache: | Englisch |
| Publikationsjahr: | 2011 |
| Verlag: | Springer-Verlag |
| Buchtitel: | Proceedings of the Second TPC Technology Conference on Performance Evaluation, Measurement and Characterization of Complex Systems |
| Reihe: | TPCTC'10 |
| Veranstaltungsort: | Singapore |
| URL / URN: | https://link.springer.com/chapter/10.1007/978-3-642-18206-8_... |
| Kurzbeschreibung (Abstract): | In many fields of research and business data sizes are breaking the petabyte barrier. This imposes new problems and research possibilities for the database community. Usually, data of this size is stored in large clusters or clouds. Although clouds have become very popular in recent years, there is only little work on benchmarking cloud applications. In this paper we present a data generator for cloud sized applications. Its architecture makes the data generator easy to extend and to configure. A key feature is the high degree of parallelism that allows linear scaling for arbitrary numbers of nodes. We show how distributions, relationships and dependencies in data can be computed in parallel with linear speed up. |
| ID-Nummer: | TUD-CS-2011-2935 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 03 Jan 2019 14:18 |
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