Fair multi-agent task allocation for large datasets analysis

Abstract : MapReduce is a design pattern for processing large datasets distributed on a cluster. Its performances are linked to the data structure and the runtime environment. Indeed, data skew can yield an unfair task allocation, but even when the initial allocation produced by the partition function is well balanced, an unfair allocation can occur during the reduce phase due to the heterogeneous performance of nodes. For these reasons, we propose an adaptive multi-agent system. In our approach, the reducer agents interact during the job and the task reallocation is based on negotiation in order to decrease the workload of the most loaded reducer and so the runtime. In this paper, we propose and evaluate two negotiation strategies. Finally, we experiment our multi-agent system with real-world datasets over heterogeneous runtime environment.
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Article dans une revue
Knowledge and Information Systems (KAIS), Springer, 2017, 54 (3), pp.591-615. 〈10.1007/s10115-017-1087-4 〉
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Contributeur : Cristal Equipe Smac <>
Soumis le : dimanche 23 juillet 2017 - 17:24:42
Dernière modification le : mardi 3 juillet 2018 - 11:22:26

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Quentin Baert, Anne-Cécile Caron, Maxime Morge, Jean-Christophe Routier. Fair multi-agent task allocation for large datasets analysis. Knowledge and Information Systems (KAIS), Springer, 2017, 54 (3), pp.591-615. 〈10.1007/s10115-017-1087-4 〉. 〈hal-01567428〉

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