Predicting the costs of serverless workflows

Simon Eismann, Johannes Grohmann, Erwin Van Eyk, Nikolas Herbst, Samuel Kounev

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.

Original languageEnglish
Title of host publicationICPE '20
Subtitle of host publicationProceedings of the ACM/SPEC International Conference on Performance Engineering
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450369916
Publication statusPublished - 20 Apr 2020
Event11th ACM/SPEC International Conference on Performance Engineering, ICPE 2020 - Edmonton, Canada
Duration: 20 Apr 202024 Apr 2020


Conference11th ACM/SPEC International Conference on Performance Engineering, ICPE 2020


  • Cost
  • Performance
  • Prediction
  • Serverless
  • Workflows


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