Performance Evaluation of Automated Multi-Service Deployment in Edge-Cloud Environments with the CODECO Toolkit
New open-source framework slashes manual effort for container orchestration across diverse hardware from ARM to Raspberry Pi.
A consortium of researchers has released a comprehensive performance evaluation of the CODECO toolkit, an open-source framework designed to tackle the persistent challenge of automating multi-service application deployment in heterogeneous Edge-Cloud environments. The study, led by Georgios Koukis and involving ten other authors, rigorously compares CODECO against standard Kubernetes (K8s) workflows using three key performance indicators: deployment time, level of manual intervention, and runtime performance with resource utilization. The experiments were conducted across a diverse range of hardware platforms, including ARM, AMD, and Raspberry Pi (RPi), and utilized various K8s distributions, including lightweight variants like k3s.
The results demonstrate that CODECO substantially reduces the manual effort required for orchestration tasks while maintaining competitive performance and introducing only acceptable overhead. This finding is significant for developers and DevOps engineers managing latency-sensitive and compute-intensive applications built on containerized microservices. By validating CODECO's effectiveness, the research highlights its potential to enhance the flexibility and intelligence of Kubernetes-based deployments, making it easier to manage applications that span from centralized cloud resources to distributed edge devices. The toolkit represents a step forward in simplifying the operational complexity of modern, distributed software architectures.
- CODECO is an open-source framework that automates deployment and management of multi-service apps in Edge-Cloud environments.
- Performance tests across ARM, AMD, and Raspberry Pi hardware showed it drastically cuts manual effort vs. baseline Kubernetes.
- The toolkit maintains competitive runtime performance and acceptable overhead when used with K8s distributions like k3s.
Why It Matters
It simplifies deploying complex, latency-sensitive applications across distributed infrastructure, reducing DevOps burden and operational costs.