Predicting the Costs of Serverless Workflows - Replication package

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. In our 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.

Reproducing the results from this paper consists of two parts reproducing the performance measurements for the audio processing functions in the Google Functions environment and reproducing the performance predictions using the proposed approach based on the collected measurement data. In order to enable quick reproduction of the performance measurements, we packaged the scripts for the experiment automation as a docker container. The original measurement data used in the paper and the python code implementing the proposed approach are available in form of a CodeOcean capsule, which enables quick and easy reproduction of our results.