On the Dynamic Shifting of the MapReduce Timeout

Abstract : MapReduce has become a relevant framework for Big Data processing in the cloud. At large-scale clouds, failures do occur and may incur unwanted performance degradation to Big Data applications. As the reliability of MapReduce depends on how well they detect and handle failures, this book chapter investigates the problem of failure detection in the MapReduce framework. The case studies of this contribution reveal that the current static timeout value is not adequate and demonstrate significant variations in the application's response time with different timeout values. While arguing that comparatively little attention has been devoted to the failure detection in the framework, the chapter presents design ideas for a new adaptive timeout.
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Chapitre d'ouvrage
Rajkumar Kannan; Raihan Ur Rasool; Hai Jin; S.R. Balasundaram. Managing and Processing Big Data in Cloud Computing, IGI Global, 2016
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https://hal.inria.fr/hal-01338393
Contributeur : Shadi Ibrahim <>
Soumis le : mardi 28 juin 2016 - 15:17:22
Dernière modification le : mercredi 11 avril 2018 - 02:00:19

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  • HAL Id : hal-01338393, version 1

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Bunjamin Memishi, Shadi Ibrahim, María S. Pérez-Hernández, Gabriel Antoniu. On the Dynamic Shifting of the MapReduce Timeout. Rajkumar Kannan; Raihan Ur Rasool; Hai Jin; S.R. Balasundaram. Managing and Processing Big Data in Cloud Computing, IGI Global, 2016. 〈hal-01338393〉

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