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\({\textsc {DDFlasks}}\): Deduplicated Very Large Scale Data Store

  • Francisco Maia
  • João Paulo
  • Fábio Coelho
  • Francisco Neves
  • José Pereira
  • Rui Oliveira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10320)

Abstract

With the increasing number of connected devices, it becomes essential to find novel data management solutions that can leverage their computational and storage capabilities. However, developing very large scale data management systems requires tackling a number of interesting distributed systems challenges, namely continuous failures and high levels of node churn. In this context, epidemic-based protocols proved suitable and effective and have been successfully used to build DataFlasks, an epidemic data store for massive scale systems. Ensuring resiliency in this data store comes with a significant cost in storage resources and network bandwidth consumption. Deduplication has proven to be an efficient technique to reduce both costs but, applying it to a large-scale distributed storage system is not a trivial task. In fact, achieving significant space-savings without compromising the resiliency and decentralized design of these storage systems is a relevant research challenge.

In this paper, we extend DataFlasks with deduplication to design DDFlasks. This system is evaluated in a real world scenario using Wikipedia snapshots, and the results are twofold. We show that deduplication is able to decrease storage consumption up to 63% and decrease network bandwidth consumption by up to 20%, while maintaining a fully-decentralized and resilient design.

Notes

Acknowledgments

The research leading to these results was part-funded by (1) Project TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020 is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF); (2) the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Portuguese Foundation for Science and Technology as part of project UID/EEA/50014/2013 and by (3) the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014–2020, under grant agreement No. 732051.

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Francisco Maia
    • 1
  • João Paulo
    • 1
  • Fábio Coelho
    • 1
  • Francisco Neves
    • 1
  • José Pereira
    • 1
  • Rui Oliveira
    • 1
  1. 1.HASLab, INESC TECUniversity of MinhoBragaPortugal

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