Predicting the costs of serverless workflows
| Autor: | Erwin van Eyk, Samuel Kounev, Johannes Grohmann, Nikolas Herbst, Simon Eismann |
|---|---|
| Přispěvatelé: | Computer Systems, Network Institute |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Expected cost
business.industry Computer science Cost Distributed computing media_common.quotation_subject Performance Serverless Response time 020206 networking & telecommunications 020207 software engineering Cloud computing 02 engineering and technology Workflow model Workflows Workflow 0202 electrical engineering electronic engineering information engineering Static response Cost prediction business Function (engineering) Prediction media_common |
| Zdroj: | Eismann, S, Grohmann, J, Van Eyk, E, Herbst, N & Kounev, S 2020, Predicting the costs of serverless workflows . in ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering . Association for Computing Machinery, Inc, pp. 265-276, 11th ACM/SPEC International Conference on Performance Engineering, ICPE 2020, Edmonton, Canada, 20/04/20 . https://doi.org/10.1145/3358960.3379133 ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering, 265-276 STARTPAGE=265;ENDPAGE=276;TITLE=ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering ICPE |
| DOI: | 10.1145/3358960.3379133 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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