Landlord: Coordinating Dynamic Software Environments to Reduce Container Sprawl

Abstract

Containers provide customizable software environments that are independent from the system on which they are deployed. Online services for task execution must often generate containers on the fly to meet user-generated requests. However, as the number of users grows and container environments are changed and updated over time, there is an explosion in the number of containers that must be managed, despite the fact that there is significant overlap among many of the containers in use. We analyze a trace of container launches on the public Binder service and demonstrate the performance and resource usage issues associated with container sprawl. We present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> , an algorithm that coalesces related container environments, and show that it can improve container reuse and reduce the number of container builds required in the Binder trace by 40%. We perform a sensitivity analysis of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> using randomized synthetic workloads on a high-energy physics (HEP) software repository and demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> shows benefits for container management across a wide range of usage patterns. Finally, we compare <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Landlord</small> to offline clustering, and observe that the continuous churn in software necessitates an online approach.