Although decentralized Version Control Systems (VCSs) like Git support several organizational structures, a central copy of the repository is typically where development activity is coalesced and where official software releases are produced. Due to growth in team size and the popularity of monolithic repositories (a.k.a., “monorepos”) that span entire organizations, central repositories are being strained. Remedial actions that devops engineers take, such as performing garbage collection routines, can backfire because they are computationally expensive and if run at an inopportune moment, may degrade repository performance or even cause the host to crash.

To sustain the performance of VCSs under production workloads, we propose a reinforcement learning agent that can take remedial actions. Since a large quantity of VCS activity is needed to train the agent, we first augment the VCS to enable a greater throughput, observing that the augmented VCS outperforms the stock VCS to a large, statistically significant degree. Then, we compare the performance that a central VCS can sustain when the agent is applied against a schedule-based garbage collection policy and a no-action baseline, observing 64 to 82-fold improvements in the Area Under the Curve (AUC) that plots repository performance over time. This paper takes a promising first step towards automatically sustaining the performance of VCSs under heavy production workloads.