The Cure is in the Cause: A Filesystem for Container Debloating

TL;DR

This paper introduces BAFFS, a filesystem-based debloating layer for containers that significantly reduces image sizes and improves startup and provisioning times, especially beneficial for resource-constrained environments.

Contribution

The paper presents BAFFS, a novel filesystem layer for container debloating that preserves layered structure and improves efficiency over existing tools.

Findings

Over 50% of top Docker containers have more than 60% bloat.

BAFFS reduces container sizes significantly while maintaining functionality.

BAFFS decreases cold start latency by up to 68% and provisioning times by up to 19%.

Abstract

Containers have become a standard for deploying applications due to their convenience, but they often suffer from significant software bloat-unused files that inflate image sizes, increase provisioning times, and waste resources. These inefficiencies are particularly problematic in serverless and edge computing scenarios, where resources are constrained, and performance is critical. Existing debloating tools are limited in scope and effectiveness, failing to address the widespread issue of container bloat at scale. In this paper, we conduct a large-scale evaluation of container bloat, analyzing the top 20 most downloaded containers on DockerHub. We evaluate two state-of-the-art debloating tools, identify their limitations, and propose a novel solution, BAFFS, which addresses bloat at the filesystem level by introducing a flexible debloating layer that preserves the layered structure of container filesystems. The debloating layer can be organized in different ways to meet diverse requirements. Our evaluation demonstrates that over 50% of the top-downloaded containers have more than 60% bloat, and BAFFS reduces container sizes significantly while maintaining functionality. For serverless functions, BAFFS reduces cold start latency by up to 68%. Additionally, when combined with lazy-loading snapshotters, BAFFS enhances provisioning efficiency, reducing conversion times by up to 93% and provisioning times by up to 19%.