Description
The Infrastructure Manager (IM) is an open-source Cloud orchestrator to deploy complex virtual application architectures (defined in TOSCA) on multiple Cloud back-ends (AWS, Azure, OpenStack, etc.), featuring a web-based UI (IM Dashboard) a CLI and a REST API. The IM is being used in production as the orchestrator in the EGI Federated Cloud, the largest distributed computing platform in Europe, composed of OpenStack sites. It is also the Application Management Layer / Deployment service of the EOSC EU Node, a public procurement action from the European Commission to build the EOSC (European Open Science Cloud), a platform to create a web of FAIR data and services for science in Europe.
There is a lack of transatlantic computational testbeds to foster science across borders on computing-related collaborations between US and EU institutions. To this aim, we decided to integrate the IM with Chameleon Cloud, a configurable experimental environment for large-scale edge-to-cloud research managed by Argonne National Lab (ANL) and including Cloud-based resources from institutions such as the University of Chicago (UC) and the Texas Advanced Computing Center (TACC). The goal is to dynamically deploy TOSCA-based application architectures on virtualized computing resources that span across large-scale distributed computing infrastructures such as EGI Cloud Compute and Chameleon Cloud. Funding was secured to do a research exchange of a UPV member at Argonne National Lab during April 2025.
This contribution summarizes the main integration activities and lessons learned, illustrating the feasibility of deploying complex application architectures via Infrastructure as Code approaches, even across multiple organizational domains via the federating capabilities offered by the Infrastructure Manager.
This exchange was funded by the DISCOVER-US project, financed by the European Union’s Horizon Europe research and innovation funding programme under grant agreement number 101135064. Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation. GM acknowledges Grant PID2020-113126RB-I00 funded by MICIU/AEI/10.13039/501100011033. This work was supported by the project AI4EOSC ‘‘Artificial Intelligence for the European Open Science Cloud’’ that has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant 101058593. Also, the project iMagine ‘‘AI-based image data analysis tools for aquatic research’’ that has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant 101058625
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