Performance Evaluation of Automated Multi-Service Deployment in Edge-Cloud Environments with the CODECO Toolkit

2026-03-08

AI Summary

This study addresses the challenge of automating the deployment of multi-service containerized applications in heterogeneous edge-cloud environments, where minimizing manual intervention while ensuring performance guarantees remains difficult. The authors evaluate and validate the open-source CODECO toolkit’s orchestration capabilities across diverse hardware platforms—including ARM, AMD, and Raspberry Pi—and lightweight Kubernetes distributions such as k3s. Experimental results demonstrate that, compared to standard Kubernetes workflows, CODECO significantly reduces human intervention during deployment while maintaining competitive deployment efficiency, runtime performance, and manageable resource overhead. These findings highlight CODECO’s strong compatibility and its potential to lower operational complexity in edge-cloud collaborative deployments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Containerized microservices are widely adopted for latency-sensitive and compute-intensive applications, with Kubernetes (K8s) as the dominant orchestration platform. However, automating the deployment and management of multi-service applications remains challenging, particularly in heterogeneous Edge-Cloud environments. This paper evaluates the CODECO toolkit, an open-source framework designed to enhance container orchestration across distributed infrastructures. We compare CODECO with baseline K8s workflows using three key performance indicators: deployment time, level of manual intervention, and runtime performance with resource utilization. Experiments across diverse hardware platforms (ARM, AMD, RPi) and K8s distributions, including lightweight variants such as k3s, demonstrate that CODECO substantially reduces manual effort while maintaining competitive performance and acceptable overhead. These results validate CODECO as an effective solution for Edge-Cloud orchestration and highlight its potential to improve the flexibility and intelligence of K8s-based deployments.

Problem

• multi-service deployment
• Edge-Cloud environments
• container orchestration
• Kubernetes
• automation

Innovation

• CODECO
• Edge-Cloud orchestration
• automated deployment
• containerized microservices
• Kubernetes