serverless
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.
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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.
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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.
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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.
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503 Views