Energy-Driven Straggler Mitigation in MapReduce - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Energy-Driven Straggler Mitigation in MapReduce


Energy consumption is an important concern for large-scale data-centers, which results in huge monetary cost for data-center operators. Due to the hardware heterogeneity and contentions between concurrent workloads, straggler mitigation is important to many Big Data applications running in large-scale data-centers and the speculative execution technique is widely-used to handle stragglers. Although a large number of studies have been proposed to improve the performance of Big Data applications using speculative execution, few of them have studied the energy efficiency of their solutions. In this paper, we propose two techniques to improve the energy efficiency of speculative executions while ensuring comparable performance. Specifically, we propose a hierarchical straggler detection mechanism which can greatly reduce the number of killed speculative copies and hence save the energy consumption. We also propose an energy-aware speculative copy allocation method which considers the trade-off between performance and energy when allocating speculative copies. We implement both techniques into Hadoop and evaluate them using representative MapReduce benchmarks. Results show that our solution can reduce the energy waste on killed speculative copies by up to 100% and improve the energy efficiency by 20% compared to state-of-the-art mechanisms.
Fichier principal
Vignette du fichier
EuroPar2017.pdf (405.73 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01560044 , version 1 (11-07-2017)



Tien-Dat Phan, Shadi Ibrahim, Amelie Chi Zhou, Guillaume Aupy, Gabriel Antoniu. Energy-Driven Straggler Mitigation in MapReduce. Euro-Par 2017 : 23rd International Conference on Parallel and Distributed Computing, Aug 2017, Santiago de Compostela, Spain. ⟨10.1007/978-3-319-64203-1_28⟩. ⟨hal-01560044⟩
638 View
331 Download



Gmail Facebook X LinkedIn More