Enabling Big Data Analytics in the Hybrid Cloud using Iterative MapReduce

Abstract : The cloud computing model has seen tremendous commercial success through its materialization via two prominent models to date, namely public and private cloud. Recently, a third model combining the former two service models as on-/off-premise resources has been receiving significant market traction: hybrid cloud. While state of art techniques that address workload performance prediction and efficient workload execution over hybrid cloud setups exist, how to address data-intensive workloads - including Big Data Analytics - in similar environments is nascent. This paper addresses this gap by taking on the challenge of bursting over hybrid clouds for the benefit of accelerating iterative MapReduce applications. We first specify the challenges associated with data locality and data movement in such setups. Subsequently, we propose a novel technique to address the locality issue, without requiring changes to the MapReduce framework or the underlying storage layer. In addition, we contribute with a performance prediction methodology that combines modeling with micro-benchmarks to estimate completion time for iterative MapReduce applications, which enables users to estimate cost-to-solution before committing extra resources from public clouds. We show through experimentation in a dual-Openstack hybrid cloud setup that our solutions manage to bring substantial improvement at predictable cost-control for two real-life iterative MapReduce applications: large-scale machine learning and text analysis.
Type de document :
Communication dans un congrès
UCC'15: The 8th IEEE/ACM International Conference on Utility and Cloud Computing, Dec 2015, Limassol, Cyprus. pp.290-299, 〈10.1109/UCC.2015.47〉
Liste complète des métadonnées

Littérature citée [26 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01207186
Contributeur : Bogdan Nicolae <>
Soumis le : mercredi 30 septembre 2015 - 12:20:15
Dernière modification le : lundi 31 octobre 2016 - 16:35:29
Document(s) archivé(s) le : jeudi 31 décembre 2015 - 10:27:14

Fichier

Enabling Big Data Analytics in...
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Francisco Clemente-Castello, Bogdan Nicolae, Kostas Katrinis, M. Mustafa Rafique, Rafael Mayo, et al.. Enabling Big Data Analytics in the Hybrid Cloud using Iterative MapReduce . UCC'15: The 8th IEEE/ACM International Conference on Utility and Cloud Computing, Dec 2015, Limassol, Cyprus. pp.290-299, 〈10.1109/UCC.2015.47〉. 〈hal-01207186〉

Partager

Métriques

Consultations de la notice

924

Téléchargements de fichiers

268