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Reliable MapReduce computing on opportunistic resources

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Abstract

MapReduce offers an ease-of-use programming paradigm for processing large data sets, making it an attractive model for opportunistic compute resources. However, unlike dedicated resources, where MapReduce has mostly been deployed, opportunistic resources have significantly higher rates of node volatility. As a consequence, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate on such volatile resources.

In this paper, we propose MOON, short for MapReduce On Opportunistic eNvironments, which is designed to offer reliable MapReduce service for opportunistic computing. MOON adopts a hybrid resource architecture by supplementing opportunistic compute resources with a small set of dedicated resources, and it extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms to take advantage of the hybrid resource architecture. Our results on an emulated opportunistic computing system running atop a 60-node cluster demonstrate that MOON can deliver significant performance improvements to Hadoop on volatile compute resources and even finish jobs that are not able to complete in Hadoop.

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Authors and Affiliations

  1. Virginia Tech, Blacksburg, USA

    Heshan Lin & Wu-chun Feng

  2. Oak Ridge National Laboratory, North Carolina State University, Raleigh, USA

    Xiaosong Ma

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  1. Heshan Lin
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  2. Xiaosong Ma
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  3. Wu-chun Feng
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Correspondence to Heshan Lin.

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Lin, H., Ma, X. & Feng, Wc. Reliable MapReduce computing on opportunistic resources. Cluster Comput 15, 145–161 (2012). https://doi.org/10.1007/s10586-011-0158-7

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