Energy-Efficient Speculative Execution using Advanced Reservation for Heterogeneous Clusters

Abstract : Many Big Data processing applications nowadays run on large-scale multi-tenant clusters. Due to hardware heterogeneity and resource contentions, straggler problem has become the norm rather than the exception in such clusters. To handle the straggler problem, speculative execution has emerged as one of the most widely used straggler mitigation techniques. Although a number of speculative execution mechanisms have been proposed, as we have observed from real-world traces, the questions of ``when'' and ``where'' to launch speculative copies have not been fully discussed and hence cause inefficiencies on the performance and energy of Big Data applications. In this paper, we propose a performance model and an energy consumption model to reveal the performance and energy variations with different speculative execution solutions. We further propose a window-based dynamic resource reservation and a heterogeneity-aware copy allocation technique to answer the ``when'' and ``where'' questions for speculative executions. Evaluations using real-world traces show that our proposed technique can improve the performance of Big Data applications by up to 30% and reduce the overall energy consumption by up to 34%.
Liste complète des métadonnées

https://hal.inria.fr/hal-01807496
Contributor : Shadi Ibrahim <>
Submitted on : Friday, September 21, 2018 - 2:01:33 PM
Last modification on : Tuesday, March 26, 2019 - 9:25:22 AM
Document(s) archivé(s) le : Saturday, December 22, 2018 - 3:24:06 PM

File

ICPP2018.pdf
Files produced by the author(s)

Identifiers

Citation

Amelie Chi Zhou, Tien-Dat Phan, Shadi Ibrahim, Bingsheng He. Energy-Efficient Speculative Execution using Advanced Reservation for Heterogeneous Clusters. ICPP 2018 - 47th International Conference on Parallel Processing, Aug 2018, Eugene, United States. pp.article n°8, ⟨10.1145/3225058.3225084⟩. ⟨hal-01807496⟩

Share

Metrics

Record views

349

Files downloads

177