Deployment of serverless functions depends on various factors. This dataset presents deployment time of a Python serverless function with various deployment package size, deployed on 6 regions of AWS and 6 regions of IBM. Deployment scripts are executed from Innsbruck, Austria.
Effects of the "spawn start" of the Monte Carlo serverless function that simulates Pi.
The functions are orchestrated as a workflow and executed with the xAFCL enactment engine (https://doi.org/10.1109/TSC.2021.3128137) on three regions (US, EU, Asia) of three cloud providers AWS Lambda, Google Cloud Functions, and IBM Cloud Functions.
100 instances of the Monte Carlo function are executed with "concurrency=30", which means that the first 30 functions are executed in parallel, while the rest 70 are invoked one after the other after some active function finishes.
Logs from running Monte Carlo simulation as serverless functions on Frankfurt, North Virginia, Tokyo regions of four FaaS systems (AWS, Google, IBM, Alibaba).
Each execution is repeated 5 times (all are warm start).
The conducted analysis is a part of a submitted manuscript to IEEE TSC.
This dataset contains the execution time of running a total of 3000 functions scattered evenly to three regions: AWS Frankfurt, IBM Frankfurt and IBM Tokyo from University of Innsbruck.
Each execution is repeated three times.
