SuperFlow: Performance Testing for Serverless Computing
- URL: http://arxiv.org/abs/2306.01620v1
- Date: Fri, 2 Jun 2023 15:29:28 GMT
- Title: SuperFlow: Performance Testing for Serverless Computing
- Authors: Jinfeng Wen, Zhenpeng Chen, Federica Sarro, Xuanzhe Liu
- Abstract summary: We propose SuperFlow, the first performance testing approach tailored specifically for serverless computing.
SuperFlow provides testing results with 97.22% accuracy, 39.91 percentage points higher than the best currently available technique.
- Score: 14.872563076658563
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Serverless computing is an emerging cloud computing paradigm that allows
software engineers to develop cloud applications as a set of functions (called
serverless functions). However, accurately obtaining the performance (i.e.,
response latency) of serverless functions is challenging due to the highly
dynamic nature of the environment in which they run. To tackle this problem, a
possible solution is to use performance testing to determine how many
repetitions of a serverless function with a given input are needed to cater to
the performance fluctuation. To this end, we conduct an empirical study of
state-of-the-art performance testing techniques for traditional cloud
applications on 65 serverless functions collected from top-tier research
venues. We find that these techniques exhibit low accuracy. Therefore, we
propose SuperFlow, the first performance testing approach tailored specifically
for serverless computing. SuperFlow incorporates an accuracy check and a
stability check to obtain accurate and reliable performance results. The
evaluation demonstrates that SuperFlow provides testing results with 97.22%
accuracy, 39.91 percentage points higher than the best currently available
technique. We have publicly released the code and data from this study to
facilitate future replication and extension.
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