I/O resource isolation of public cloud serverless function runtimes for data-intensive applications

Abstract

Serverless computing and a function execution model, Function-as-a-Service (FaaS), are currently receiving considerable attention from both academia and industry. One of the reasons for the success of serverless computing is its straightforward interface that abstracts complex internals of cloud computing resource usage and configurations. However, this approach may result in hiding too much information about how underlying cloud resources would work, entailing that users cannot predict how their applications will perform, especially for IO-heavy ones. To address this issue, we evaluate several aspects of network and disk IO performance with realistic workloads using public FaaS systems. Our analysis reveals that current public FaaS systems do not provide appropriate levels of IO performance differentiation, and the ability to isolate network resource allocation during concurrent execution is rarely offered by service providers. Based on the results presented in this paper, we insist that it must be mandatory for network and disk IO resource performance of FaaS to be more visible and predictable, as is the case for memory and CPU, in order to expand serverless computing applications to data-intensive ones.

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    https://snap.stanford.edu/data/web-FineFoods.html.

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Acknowledgements

This work is supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP, Nos. NRF-2015R1A5A7037615 and NRF-2016R1C1B2015135), the ICT R&D Program of IITP (2017-0-00396), and the AWS Cloud Credits for Research Program.

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Correspondence to Kyungyong Lee.

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Kim, J., Lee, K. I/O resource isolation of public cloud serverless function runtimes for data-intensive applications. Cluster Comput 23, 2249–2259 (2020). https://doi.org/10.1007/s10586-020-03103-4

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Keywords

  • FaaS
  • Serverless computing
  • Resource isolation
  • Cloud functions
  • Data-intensive application
  • I/O resource