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Cross-Phase Optimization in MapReduce

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Cloud Computing for Data-Intensive Applications

Abstract

MapReduce has proven remarkably effective for a wide variety of data-intensive applications, but it was designed to run on large single-site homogeneous clusters. Researchers have begun to explore the extent to which the original MapReduce assumptions can be relaxed including skewed workloads, iterative applications, and heterogeneous computing environments. This chapter continues this exploration by applying MapReduce across widely distributed data over distributed computation resources. This problem arises when datasets are generated and stored at multiple sites as is common in many scientific domains and increasingly e-commerce applications. It also occurs when multi-site resources such as geographically separated data centers are applied to the same application. Using Hadoop, we show that the absence of network and node homogeneity and locality of data lead to poor performance. The problem is that interaction of MapReduce phases becomes pronounced in the presence of heterogeneous network behavior. In this paper, we propose new cross-phase optimization techniques that enable independent MapReduce phases to influence one another. We propose techniques that optimize the push and map phases to enable push-map overlap and to allow map behavior to feed back into push dynamics. Similarly, we propose techniques that optimize the map and reduce phases to enable shuffle cost to feed back and affect map scheduling decisions. We evaluate the benefits of our techniques in both Amazon EC2 and PlanetLab. The experimental results show the potential of these techniques as performance is improved from 7 to 18 % depending on the execution environment and application.

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Notes

  1. 1.

    http://newsroom.fb.com.

  2. 2.

    When this is not possible, tasks must read their inputs remotely, and scarce network bandwidth becomes a limiting factor.

  3. 3.

    This and the remaining figures and tables from this chapter are reproduced from [23].

  4. 4.

    To improve fault tolerance, we have also added an option to cache and replicate inputs at the compute nodes. This reduces the need to re-fetch remote data after task failures or for speculative execution.

  5. 5.

    Throughout this chapter, error bars indicate 95 % confidence intervals.

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Acknowledgements

The authors would like to acknowledge Professor Ramesh Sitaraman (ramesh@cs.umass.edu) for his contributions to our model-driven optimization approach (Sect. 2), as well as Chenyu Wang (chwang@cs.umn.edu) for his contributions to the cross-phase optimization techniques. They would further like to acknowledge NSF Grants IIS-0916425 and CNS-0643505, which supported this research.

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Authors and Affiliations

  1. University of Minnesota, Keller Hall 4-192, 200 Union Street SE, Minneapolis, MN, 55455, USA

    Benjamin Heintz, Abhishek Chandra & Jon Weissman

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  1. Benjamin Heintz
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Correspondence to Benjamin Heintz .

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  1. University of Florida, Gainesville, Florida, USA

    Xiaolin Li

  2. Indiana University, Bloomington, Indiana, USA

    Judy Qiu

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Heintz, B., Chandra, A., Weissman, J. (2014). Cross-Phase Optimization in MapReduce. In: Li, X., Qiu, J. (eds) Cloud Computing for Data-Intensive Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1905-5_12

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  • DOI: https://doi.org/10.1007/978-1-4939-1905-5_12

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