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J2M: a Java to MapReduce translator for cloud computing

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Abstract

Cloud computing has gradually evolved into an infrastructural tool for a variety of scientific research and computing. It has become a trend that lots of products have been migrated from local servers to cloud by many institutions and organizations. One of the challenges in cloud computing now is how to run software efficiently on cloud platforms since lots of original codes are not capable of being executed in parallel on cloud contexts, resulting in that the power of clouds cannot be exerted well. It is costly to redesign and convert current sequential codes into cloud platform. Thus, automatic translation from sequential code to cloud code is one of the directions that could be taken to resolve the problem of code migration in cloud infrastructure. In this paper, a new Java to MapReduce (J2M) translator is developed to achieve the automatic translation from sequential Java to cloud for specific data-parallel code with large loops. This paper will provide details about the design of our translator and evaluate our performance through experiments. The experimental results not only indicate that the translator can precisely translate the sequential Java into cloud codes, but also show that it can achieve very good speedup in performance, and we expect that an almost linear speedup is possible if larger enough data is processed. It is believed that the J2M translator is an ideal stereotype for code migration and will play an important role in the transition era of cloud computing.

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Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA

    Bing Li, Ning Yu & Yi Pan

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Junbo Zhang

Authors
  1. Bing Li
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  2. Junbo Zhang
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  3. Ning Yu
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  4. Yi Pan
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Correspondence to Yi Pan.

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Li, B., Zhang, J., Yu, N. et al. J2M: a Java to MapReduce translator for cloud computing. J Supercomput 72, 1928–1945 (2016). https://doi.org/10.1007/s11227-016-1695-x

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