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European Conference on Service-Oriented and Cloud Computing

ESOCC 2022: Service-Oriented and Cloud Computing pp 137–154Cite as

MAFF: Self-adaptive Memory Optimization for Serverless Functions

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13226))

Abstract

Function-as-a-Service (FaaS), a key enabler of serverless computing, has been proliferating, as it offers a cheap alternative for application development and deployment. However, while offering many advantages, FaaS also poses new challenges. In particular, most commercial FaaS providers still require users to manually configure the memory allocated to the FaaS functions based on their experience and knowledge. This often leads to suboptimal function performance and higher execution costs. In this paper, we present a framework called MAFF that automatically finds the optimal memory configurations for the FaaS functions based on two optimization objectives: cost-only and balanced (balance between cost and execution duration). Furthermore, MAFF self-adapts the memory configurations for the FaaS functions based on the changing function inputs or other requirements, such as an increase in the number of requests. Moreover, we propose and implement different optimization algorithms for different objectives. We demonstrate the functionality of MAFF on AWS Lambda by testing on four different categories of FaaS functions. Our results show that the suggested memory configurations with the Linear algorithm achieve 90% accuracy with a speedup of 2x compared to the other algorithms. Finally, we compare MAFF with two popular memory optimization tools provided by AWS, i.e., AWS Compute Optimizer and AWS Lambda Power Tuning, and demonstrate how our framework overcomes their limitations.

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Notes

  1. 1.

    https://github.com/tetzubko/self-adaptive-memory-faas.

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Acknowledgements

This work was supported by the funding of the German Federal Ministry of Education and Research (BMBF) in the scope of the Software Campus program. The authors also thank the anonymous reviewers whose comments helped in improving this paper.

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Authors and Affiliations

  1. Chair of Computer Architecture and Parallel Systems, Technical University of Munich, Garching, Germany

    Tetiana Zubko, Anshul Jindal, Mohak Chadha & Michael Gerndt

Authors
  1. Tetiana Zubko
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  2. Anshul Jindal
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  3. Mohak Chadha
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  4. Michael Gerndt
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Corresponding author

Correspondence to Anshul Jindal .

Editor information

Editors and Affiliations

  1. University of Southern Denmark, Odense, Denmark

    Fabrizio Montesi

  2. University of Cyprus, Nicosia, Cyprus

    George Angelos Papadopoulos

  3. Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Wolf Zimmermann

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Zubko, T., Jindal, A., Chadha, M., Gerndt, M. (2022). MAFF: Self-adaptive Memory Optimization for Serverless Functions. In: Montesi, F., Papadopoulos, G.A., Zimmermann, W. (eds) Service-Oriented and Cloud Computing. ESOCC 2022. Lecture Notes in Computer Science, vol 13226. Springer, Cham. https://doi.org/10.1007/978-3-031-04718-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-04718-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04717-6

  • Online ISBN: 978-3-031-04718-3

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