Is it time to revisit Erasure Coding in Data-intensive clusters? - LS2N - équipe Stack Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Is it time to revisit Erasure Coding in Data-intensive clusters?

Résumé

Data-intensive clusters are heavily relying on distributed storage systems to accommodate the unprecedented growth of data. Hadoop distributed file system (HDFS) is the primary storage for data analytic frameworks such as Spark and Hadoop. Traditionally, HDFS operates under replication to ensure data availability and to allow locality-aware task execution of data-intensive applications. Recently, erasure coding (EC) is emerging as an alternative method to replication in storage systems due to the continuous reduction in its computation overhead. In this work, we conduct an extensive experimental study to understand the performance of data-intensive applications under replication and EC. We use representative benchmarks on the Grid'5000 testbed to evaluate how analytic workloads, data persistency, failures, the back-end storage devices, and the network configuration impact their performances. Our study sheds the light not only on the potential benefits of erasure coding in data-intensive clusters but also on the aspects that may help to realize it effectively.
Fichier principal
Vignette du fichier
MASCOTS CR.pdf (892 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02263116 , version 1 (02-08-2019)

Identifiants

Citer

Jad Darrous, Shadi Ibrahim, Christian Pérez. Is it time to revisit Erasure Coding in Data-intensive clusters?. MASCOTS 2019 - 27th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Oct 2019, Rennes, France. pp.165-178, ⟨10.1109/MASCOTS.2019.00026⟩. ⟨hal-02263116⟩
173 Consultations
853 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More