Abstract
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 language | English |
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Title of host publication | ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering |
Publisher | Association for Computing Machinery, Inc |
Pages | 265-276 |
Number of pages | 12 |
ISBN (Electronic) | 9781450369916 |
DOIs | |
Publication status | Published - Apr 2020 |
Event | 11th ACM/SPEC International Conference on Performance Engineering, ICPE 2020 - Edmonton, Canada Duration: 20 Apr 2020 → 24 Apr 2020 |
Conference
Conference | 11th ACM/SPEC International Conference on Performance Engineering, ICPE 2020 |
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Country/Territory | Canada |
City | Edmonton |
Period | 20/04/20 → 24/04/20 |
Funding
This material is based upon work supported by Google Cloud. The authors would like to thank the anonymous reviewers for their valuable feedback and literature suggestions.
Keywords
- Cost
- Performance
- Prediction
- Serverless
- Workflows