Enabling Big Data Analytics in the Hybrid Cloud using Iterative MapReduce

Francisco Clemente-Castello (1) , Bogdan Nicolae (2) , Kostas Katrinis (2) , M. Mustafa Rafique (2) , Rafael Mayo (1) , Juan Carlos Fernandez (1) , Daniela Loreti (3)

1 Universidad Jaume I
2 IBM Research - Ireland
3 UNIBO - Alma Mater Studiorum Università di Bologna = University of Bologna

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.

Mots clés: Performance Prediction; Hybrid Cloud; Big Data Analytics; Iterative Applications; MapReduce; Data locality

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

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⟩