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A Data Generator for Cloud-scale Benchmarking

Rabl, Tilmann and Frank, Michael and Sergieh, Hatem Mousselly and Kosch, Harald (2011):
A Data Generator for Cloud-scale Benchmarking.
In: Proceedings of the Second TPC Technology Conference on Performance Evaluation, Measurement and Characterization of Complex Systems, Springer-Verlag, Singapore, In: TPCTC'10, ISBN 978-3-642-18205-1,
[Online-Edition: https://link.springer.com/chapter/10.1007/978-3-642-18206-8_...],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2011
Creators: Rabl, Tilmann and Frank, Michael and Sergieh, Hatem Mousselly and Kosch, Harald
Title: A Data Generator for Cloud-scale Benchmarking
Language: English
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.

Title of Book: Proceedings of the Second TPC Technology Conference on Performance Evaluation, Measurement and Characterization of Complex Systems
Series Name: TPCTC'10
Publisher: Springer-Verlag
ISBN: 978-3-642-18205-1
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Location: Singapore
Date Deposited: 31 Dec 2016 14:29
Official URL: https://link.springer.com/chapter/10.1007/978-3-642-18206-8_...
Identification Number: TUD-CS-2011-2935
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