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On the Dynamic Shifting of the MapReduce Timeout

On the Dynamic Shifting of the MapReduce Timeout

Bunjamin Memishi, Shadi Ibrahim, Maria S. Perez, Gabriel Antoniu
Copyright: © 2016 |Pages: 22
ISBN13: 9781466697676|ISBN10: 1466697679|EISBN13: 9781466697683
DOI: 10.4018/978-1-4666-9767-6.ch001
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MLA

Memishi, Bunjamin, et al. "On the Dynamic Shifting of the MapReduce Timeout." Managing and Processing Big Data in Cloud Computing, edited by Rajkumar Kannan, et al., IGI Global, 2016, pp. 1-22. https://doi.org/10.4018/978-1-4666-9767-6.ch001

APA

Memishi, B., Ibrahim, S., Perez, M. S., & Antoniu, G. (2016). On the Dynamic Shifting of the MapReduce Timeout. In R. Kannan, R. Rasool, H. Jin, & S. Balasundaram (Eds.), Managing and Processing Big Data in Cloud Computing (pp. 1-22). IGI Global. https://doi.org/10.4018/978-1-4666-9767-6.ch001

Chicago

Memishi, Bunjamin, et al. "On the Dynamic Shifting of the MapReduce Timeout." In Managing and Processing Big Data in Cloud Computing, edited by Rajkumar Kannan, et al., 1-22. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9767-6.ch001

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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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