Energy-Efficient Speculative Execution using Advanced Reservation for Heterogeneous Clusters

Amelie Chi Zhou 1 Tien-Dat Phan 2 Shadi Ibrahim 3, 4 Bingsheng He 5
1 Shenzhen Univerisity [Shenzhen]
2 ENS Rennes - École normale supérieure - Rennes
3 STACK - Software Stack for Massively Geo-Distributed Infrastructures
Inria Rennes – Bretagne Atlantique , LS2N - Laboratoire des Sciences du Numérique de Nantes
4 DAPI - Département Automatique, productique et informatique
5 NTU - School of Computer Engineering [Singapore]
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%.
Type de document :
Communication dans un congrès
ICPP 2018 - 47th International Conference on Parallel Processing, Aug 2018, Eugene, United States. ACM, pp.article n°8, 〈10.1145/3225058.3225084〉
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https://hal.inria.fr/hal-01807496
Contributeur : Shadi Ibrahim <>
Soumis le : vendredi 21 septembre 2018 - 14:01:33
Dernière modification le : mardi 18 décembre 2018 - 01:24:49

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LS2N-STACK | UNIV-NANTES | INRIA | UNAM | LS2N | IMT-ATLANTIQUE

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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. ACM, pp.article n°8, 〈10.1145/3225058.3225084〉. 〈hal-01807496〉

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