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Chive: A simulation tool for epidemic data replication protocols benchmarking

  • A. Jiménez-Yáñez*
  • , J. Navarro
  • , F. D. Muñoz-Escoí
  • , I. Arrieta-Salinas
  • , J. E. Armendáriz-Iñigo
  • *Corresponding author for this work

Research output: Book chapterConference contributionpeer-review

Abstract

Epidemic data replication protocols are an interesting approach to address the scalability limitations of classic distributed databases. However, devising a system layout that takes full advantage of epidemic replication is a challenging task due to the high number of associated configuration parameters (e.g., replication layers, number of replicas per layer, etc.). The purpose of this paper is to present a Java-based simulation tool that simulates the execution of epidemic data replication protocols on user-defined configurations under different workloads. Conducted experiments show that by using the proposed approach (1) the internal dynamics of epidemic data replication protocols under a specific scenario are better understood, (2) the distributed database system design process is considerably speeded up, and (3) different system configurations can be rapidly prototyped.

Original languageEnglish
Title of host publicationICSOFT-EA 2014 - Proceedings of the 9th International Conference on Software Engineering and Applications
EditorsAndreas Holzinger, Therese Libourel, Leszek Maciaszek, Leszek Maciaszek, Stephen Mellor
PublisherSciTePress
Pages428-436
Number of pages9
ISBN (Electronic)9789897580369
DOIs
Publication statusPublished - 2014
Event9th International Conference on Software Engineering and Applications, ICSOFT-EA 2014 - Vienna, Austria
Duration: 29 Aug 201431 Aug 2014

Publication series

NameICSOFT-EA 2014 - Proceedings of the 9th International Conference on Software Engineering and Applications

Conference

Conference9th International Conference on Software Engineering and Applications, ICSOFT-EA 2014
Country/TerritoryAustria
CityVienna
Period29/08/1431/08/14

Keywords

  • Consistency
  • Epidemic data replication
  • Performance evaluation
  • Scalable datastore

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