CIR: Lightweight Container Image for Cross-Platform Deployment

2026-04-11

AI Summary

This work addresses the inefficiencies of traditional container images, which require separate builds for each target platform—leading to substantial storage and network overheads and complex maintenance, particularly for cross-platform machine learning applications in Python or R. To overcome these limitations, the authors propose a lazy-build approach featuring a novel intermediate representation format, CIR, which encapsulates only direct application dependency identifiers and defers platform-specific adaptation until deployment. At runtime, a lazy-builder dynamically assembles the complete dependency stack, effectively decoupling application code from the underlying execution environment. This design significantly streamlines multi-platform deployment workflows. Experimental results demonstrate that CIR-based images reduce image size by up to 95% compared to conventional approaches and accelerate deployment by 40–60%, outperforming mainstream container systems such as Docker, Buildah, and Apptainer.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In modern cloud and heterogeneous distributed infrastructures, container images are widely used as the deployment unit for machine learning applications. An image bundles the application with its entire platform-specific execution environment and can be directly launched into a container instance. However, this approach forces developers to build and maintain separate images for each target deployment platform. This limitation is particularly evident for widely used interpreted languages such as Python and R in data analytics and machine learning, where application code is inherently cross-platform, yet the runtime dependencies are highly platform-specific. With emerging computing paradigms such as sky computing and edge computing, which demand seamless workload migration and cross-platform deployment, traditional images not only introduce inefficiencies in storage and network usage, but also impose substantial burdens on developers, who must repeatedly craft and manage platform-specific builds. To address these challenges, we propose a lazy-build approach that defers platform-specific construction to the deployment stage, thus keeping the image itself cross-platform. To enable this, we introduce a new image format, CIR (Container Intermediate Representation), together with its pre-builder and lazy-builder. CIR targets interpreted-language applications and only stores the identifiers of the application's direct dependencies, leaving platform adaptation to the lazy-builder, which at deployment time assembles the actual dependencies into runnable containers. A single CIR can therefore be deployed across heterogeneous platforms while reducing image size by 95% compared to conventional images that bundle all dependencies. In our evaluation, CIR reduces deployment time by 40-60% compared with pre-built images, outperforming state-of-the-art systems such as Docker, Buildah, and Apptainer.

Problem

• container image
• cross-platform deployment
• platform-specific dependencies
• interpreted languages
• heterogeneous infrastructure

Innovation

• CIR
• lazy-build
• cross-platform deployment
• container image
• interpreted languages