Understanding the performance of container execution environments Guillaume Everarts de Velp, Etienne Rivière and Ramin Sadre
[email protected] EPL, ICTEAM, UCLouvain, Belgium Abstract example is an automatic grading platform named INGIn- Many application server backends leverage container tech- ious [2, 3]. This platform is used extensively at UCLouvain nologies to support workloads formed of short-lived, but and other institutions around the world to provide computer potentially I/O-intensive, operations. The latency at which science and engineering students with automated feedback container-supported operations complete impacts both the on programming assignments, through the execution of se- users’ experience and the throughput that the platform can ries of unit tests prepared by instructors. It is necessary that achieve. This latency is a result of both the bootstrap and the student code and the testing runtime run in isolation from execution time of the containers and is impacted greatly by each others. Containers answer this need perfectly: They the performance of the I/O subsystem. Configuring appro- allow students’ code to run in a controlled and reproducible priately the container environment and technology stack environment while reducing risks related to ill-behaved or to obtain good performance is not an easy task, due to the even malicious code. variety of options, and poor visibility on their interactions. Service latency is often the most important criteria for We present in this paper a benchmarking tool for the selecting a container execution environment. Slow response multi-parametric study of container bootstrap time and I/O times can impair the usability of an edge computing infras- performance, allowing us to understand such interactions tructure, or result in students frustration in the case of IN- within a controlled environment.