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FaaStest - Machine Learning Based Cost and Performance FaaS Optimization

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Economics of Grids, Clouds, Systems, and Services (GECON 2018)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11113))

Abstract

With the emergence of Function-as-a-Service (FaaS) in the cloud, pay-per-use pricing models became available along with the traditional fixed price model for VMs and increased the complexity of selecting the optimal platform for a given service. We present FaaStest - an autonomous solution for cost and performance optimization of FaaS services by taking a hybrid approach - learning the behavioral patterns of the service and dynamically selecting the optimal platform. Moreover, we combine a prediction based solution for reducing cold starts of FaaS services. Experiments present a reduction of over 50% in cost and over 90% in response time for FaaS calls.

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Author information

Authors and Affiliations

  1. School of Computer Science, College of Management Academic Studies, Rishon LeZion, Israel

    Shay Horovitz, Roei Amos, Ohad Baruch, Tomer Cohen, Tal Oyar & Afik Deri

Authors
  1. Shay Horovitz
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  2. Roei Amos
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  3. Ohad Baruch
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  4. Tomer Cohen
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  5. Tal Oyar
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  6. Afik Deri
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Corresponding author

Correspondence to Shay Horovitz .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Pisa, Italy

    Massimo Coppola

  2. National Research Council of Italy, Pisa, Italy

    Emanuele Carlini

  3. National Research Council of Italy, Genoa, Italy

    Daniele D’Agostino

  4. Seoul National University, Seoul, Korea (Republic of)

    Jörn Altmann

  5. University of Zaragoza, Zaragoza, Spain

    José Ángel Bañares

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Horovitz, S., Amos, R., Baruch, O., Cohen, T., Oyar, T., Deri, A. (2019). FaaStest - Machine Learning Based Cost and Performance FaaS Optimization. In: Coppola, M., Carlini, E., D’Agostino, D., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2018. Lecture Notes in Computer Science(), vol 11113. Springer, Cham. https://doi.org/10.1007/978-3-030-13342-9_15

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  • DOI: https://doi.org/10.1007/978-3-030-13342-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13341-2

  • Online ISBN: 978-3-030-13342-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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