Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact)

Bo Zhang 1 Filip Křikava 2 Romain Rouvoy 1, 3 Lionel Seinturier 1
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Arising with the popularity of Hadoop, optimizing Hadoop executions has grabbed lots of attention from research community. Many research contributions are proposed to elevate Hadoop performance, particularly in the domain of self-adaptive software systems. However, due to the complexity of Hadoop operation and the di culty to reproduce experiments, the efforts of these Hadoop-related research are hard to be evaluated. To address this limitation, we propose a research acceleration platform for rapid prototyping and evaluation of self-adaptive behavior in Hadoop clusters. It provides an automated manner to quickly and easily provision reproducible Hadoop environments and execute acknowledged benchmarks. This platform is based on the state-of-the-art container technology that supports both distributed configurations as well as standalone single-host setups. We demonstrate the approach on a complete implementation of a concrete Hadoop self-adaptive case study.
Type de document :
Autre publication
Artifact. 2017, pp.3. 〈10.4230/DARTS.3.1.1〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01525304
Contributeur : Romain Rouvoy <>
Soumis le : vendredi 19 mai 2017 - 18:54:05
Dernière modification le : mardi 3 juillet 2018 - 11:36:06

Identifiants

Citation

Bo Zhang, Filip Křikava, Romain Rouvoy, Lionel Seinturier. Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact). Artifact. 2017, pp.3. 〈10.4230/DARTS.3.1.1〉. 〈hal-01525304〉

Partager

Métriques

Consultations de la notice

251