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Benchmarking Dependability of MapReduce Systems

Abstract : MapReduce is a popular programming model for distributed data processing. Extensive research has been con- ducted on the reliability of MapReduce, ranging from adaptive and on-demand fault-tolerance to new fault-tolerance models. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce fault-tolerance solutions have been evaluated using microbenchmarks in an ad-hoc and overly simplified setting, which may not be representative of real-world applications. This paper presents MRBS, a comprehensive benchmark suite for evaluating the dependability of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to inject various types of faults at different rates. It also considers different application workloads and dataloads, and produces extensive reliability, availability and performance statistics. We illustrate the use of MRBS with Hadoop clusters running on Amazon EC2, and on a private cloud.
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Contributor : Sara Bouchenak Connect in order to contact the contributor
Submitted on : Friday, February 21, 2014 - 5:27:09 PM
Last modification on : Thursday, October 21, 2021 - 3:53:28 AM


  • HAL Id : hal-00950645, version 1



Amit Sangroya, Damián Serrano, Sara Bouchenak. Benchmarking Dependability of MapReduce Systems. The 31st IEEE International Symposium on Reliable Distributed Systems (SRDS), Oct 2012, Irvine, California, United States. ⟨hal-00950645⟩



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