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Building a Wide-Area File Transfer Performance Predictor: An Empirical Study

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Machine Learning for Networking (MLN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11407))

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Abstract

Wide-area data transfer is central to geographically distributed scientific workflows. Faster delivery of data is important for these workflows. Predictability is equally (or even more) important. With the goal of providing a reasonably accurate estimate of data transfer time to improve resource allocation & scheduling for workflows and enable end-to-end data transfer optimization, we apply machine learning methods to develop predictive models for data transfer times over a variety of wide area networks. To build and evaluate these models, we use 201,388 transfers, involving 759 million files totaling 9 PB transferred, over 115 heavily used source-destination pairs (“edges”) between 135 unique endpoints. We evaluate the models for different retraining frequencies and different window size of history data. In the best case, the resulting models have a median prediction error of \(\le \)21% for 50% of the edges, and \(\le \)32% for 75% of the edges. We present a detailed analysis of these results that provides insights into the cause of some of the high errors. We envision that the performance predictor will be informative for scheduling geo-distributed workflows. The insights also suggest obvious directions for both further analysis and transfer service optimization.

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Acknowledgments

This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory.

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Authors and Affiliations

  1. Argonne National Laboratory, 9700 Cass Avenue, Lemont, IL, 60439, USA

    Zhengchun Liu, Rajkumar Kettimuthu, Prasanna Balaprakash & Ian Foster

  2. University of Chicago, Chicago, IL, 60637, USA

    Zhengchun Liu, Rajkumar Kettimuthu & Ian Foster

  3. Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA

    Nageswara S. V. Rao

Authors
  1. Zhengchun Liu
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  2. Rajkumar Kettimuthu
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  3. Prasanna Balaprakash
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  4. Nageswara S. V. Rao
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  5. Ian Foster
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Corresponding author

Correspondence to Zhengchun Liu .

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Editors and Affiliations

  1. Télécom SudParis, Évry, France

    Éric Renault

  2. Inria, Paris, France

    Paul Mühlethaler

  3. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

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Liu, Z., Kettimuthu, R., Balaprakash, P., Rao, N.S.V., Foster, I. (2019). Building a Wide-Area File Transfer Performance Predictor: An Empirical Study. In: Renault, É., Mühlethaler, P., Boumerdassi, S. (eds) Machine Learning for Networking. MLN 2018. Lecture Notes in Computer Science(), vol 11407. Springer, Cham. https://doi.org/10.1007/978-3-030-19945-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-19945-6_5

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