An Effective Docker Image Slimming Approach Based on Source Code Data Dependency Analysis

TL;DR

This paper introduces { extdelta}-SCALPEL, a novel Docker image slimming method that uses static data dependency analysis to effectively reduce image size by up to 61.4% without compromising functionality.

Contribution

The paper presents a new image slimming approach based on static data dependency analysis and a command linked list data structure, improving dependency extraction accuracy.

Findings

Reduces Docker image size by up to 61.4%.

Ensures normal operation of projects after slimming.

Evaluated on 20 NPM projects and Docker images.

Abstract

Containerization is the mainstream of current software development, which enables software to be used across platforms without additional configuration of running environment. However, many images created by developers are redundant and contain unnecessary code, packages, and components. This excess not only leads to bloated images that are cumbersome to transmit and store but also increases the attack surface, making them more vulnerable to security threats. Therefore, image slimming has emerged as a significant area of interest. Nevertheless, existing image slimming technologies face challenges, particularly regarding the incomplete extraction of environment dependencies required by project code. In this paper, we present a novel image slimming model named {\delta}-SCALPEL. This model employs static data dependency analysis to extract the environment dependencies of the project code and utilizes a data structure called the command linked list for modeling the image's file system. We select 20 NPM projects and two official Docker Hub images to construct a dataset for evaluating {\delta}-SCALPEL. The evaluation results show that {\delta}-SCALPEL can reduce image sizes by up to 61.4% while ensuring the normal operation of these projects.