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Gibbon: An Availability Evaluation Framework for Distributed Databases

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On the Move to Meaningful Internet Systems. OTM 2017 Conferences (OTM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10574))

Abstract

Driven by new application domains, the database management systems (DBMSs) landscape has significantly evolved from single node DBMS to distributed database management systems (DDBMSs). In parallel, cloud computing became the preferred solution to run distributed applications. Hence, modern DDBMSs are designed to run in the cloud. Yet, in distributed systems the probability of failures is the higher the more entities are involved and by using cloud resources the probability of failures increases even more. Therefore, DDBMSs apply data replication across multiple nodes to provide high availability. Yet, high availability limits consistency or partition tolerance as stated by the CAP theorem. As the decision for two of the three attributes in not binary, the heterogeneous landscape of DDBMSs gets even more complex when it comes to their high availability mechanisms. Hence, the selection of a high available DDBMS to run in the cloud becomes a very challenging task, as supportive evaluation frameworks are not yet available. In order to ease the selection and increase the trust in running DDBMSs in the cloud, we present the Gibbon framework, a novel availability evaluation framework for DDBMSs. Gibbon defines quantifiable availability metrics, a customisable evaluation methodology and a novel evaluation framework architecture. Gibbon is discussed by an availability evaluation of MongoDB, analysing the take over and recovery time.

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Notes

  1. 1.

    https://www.mysql.com/products/cluster/.

  2. 2.

    http://cloudiator.org/.

  3. 3.

    https://aws.amazon.com/ec2/.

  4. 4.

    https://cloud.google.com/compute/.

  5. 5.

    https://azure.microsoft.com.

  6. 6.

    https://www.openstack.org/.

  7. 7.

    https://www.mongodb.com/.

  8. 8.

    http://db-engines.com/en/ranking_trend.

  9. 9.

    https://github.com/dice-project/DICE-Fault-Injection-Tool.

  10. 10.

    https://github.com/jepsen-io/jepsen.

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Acknowledgements

The research leading to these results has received funding from the EC’s Framework Programme HORIZON 2020 under grant agreement numbers 644690 (CloudSocket) and 731664 (MELODIC). We also thank Daimler TSS for the encouraging and fruitful discussions on the topic.

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Authors and Affiliations

  1. Institute of Information Resource Management, Ulm University, Ulm, Germany

    Daniel Seybold, Christopher B. Hauser, Simon Volpert & Jörg Domaschka

Authors
  1. Daniel Seybold
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  2. Christopher B. Hauser
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  3. Simon Volpert
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  4. Jörg Domaschka
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Corresponding author

Correspondence to Daniel Seybold .

Editor information

Editors and Affiliations

  1. University of Lorraine, Nancy, France

    Hervé Panetto

  2. Odisee University College, Brussels, Belgium

    Christophe Debruyne

  3. Télécom SudParis, Évry, France

    Walid Gaaloul

  4. Tilburg University, Tilburg, The Netherlands

    Mike Papazoglou

  5. Freie Universität Berlin and Fraunhofer FOKUS, Berlin, Germany

    Adrian Paschke

  6. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  7. TU Graz, Graz, Austria

    Robert Meersman

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Seybold, D., Hauser, C.B., Volpert, S., Domaschka, J. (2017). Gibbon: An Availability Evaluation Framework for Distributed Databases. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-69459-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69458-0

  • Online ISBN: 978-3-319-69459-7

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