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Fragmented ARES: Dynamic Storage for Large Objects

Authors Chryssis Georgiou , Nicolas Nicolaou , Andria Trigeorgi



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Author Details

Chryssis Georgiou
  • University of Cyprus, Nicosia, Cyprus
Nicolas Nicolaou
  • Algolysis Ltd, Limassol, Cyprus
Andria Trigeorgi
  • University of Cyprus, Nicosia, Cyprus

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Chryssis Georgiou, Nicolas Nicolaou, and Andria Trigeorgi. Fragmented ARES: Dynamic Storage for Large Objects. In 36th International Symposium on Distributed Computing (DISC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 246, pp. 25:1-25:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.DISC.2022.25

Abstract

Data availability is one of the most important features in distributed storage systems, made possible by data replication. Nowadays data are generated rapidly and developing efficient, scalable and reliable storage systems has become one of the major challenges for high performance computing. In this work, we develop and prove correct a dynamic, robust and strongly consistent distributed shared memory suitable for handling large objects (such as files) and utilizing erasure coding. We do so by integrating an Adaptive, Reconfigurable, Atomic memory framework, called Ares, with the CoBFS framework, which relies on a block fragmentation technique to handle large objects. With the addition of Ares, we also enable the use of an erasure-coded algorithm to further split the data and to potentially improve storage efficiency at the replica servers and operation latency. Our development is complemented with an in-depth experimental evaluation on the Emulab and AWS EC2 testbeds, illustrating the benefits of our approach, as well as interesting tradeoffs.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Distributed algorithms
Keywords
  • Distributed storage
  • Large objects
  • Strong consistency
  • High access concurrency
  • Erasure code
  • Reconfiguration

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