CC BY 4.0DesLauriers, JamesJamesDesLauriersKovacs, JozsefJozsefKovacsKiss, TamasTamasKissStork, AndreAndreStorkPena Serna, SebastianSebastianPena SernaUllah, AmjadAmjadUllah2024-12-192024-12-192025https://doi.org/10.24406/publica-4005https://publica.fraunhofer.de/handle/publica/48088310.1016/j.future.2024.10762810.24406/publica-4005With the emergence of Internet of Things (IoT) devices collecting large amounts of data at the edges of the network, a new generation of hyper-distributed applications is emerging, spanning cloud, fog, and edge computing resources. The automated deployment and management of such applications requires orchestration tools that take a deployment descriptor (e.g. Kubernetes manifest, Helm chart or TOSCA) as input, and deploy and manage the execution of applications at run-time. While most deployment descriptors are prepared by a single person or organisation at one specific time, there are notable scenarios where such descriptors need to be created collaboratively by different roles or organisations, and at different times of the application’s life cycle. An example of this scenario is the modular development of digital twins, composed of the basic building blocks of data, model and algorithm. Each of these building blocks can be created independently from each other, by different individuals or companies, at different times. The challenge here is to compose and build a deployment descriptor from these individual components automatically. This paper presents a novel solution to automate the collaborative composition and generation of deployment descriptors for distributed applications within the cloud-to-edge continuum. The implemented solution has been prototyped in over 25 industrial use cases within the DIGITbrain project, one of which is described in the paper as a representative example.enBranche: Automotive IndustryResearch Line: (Interactive) simulation (SIM)Research Line: Modeling (MOD)LTA: Interactive decision-making support and assistance systemsLTA: Monitoring and control of processes and systemsLTA: Scalable architectures for massive data setsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)Cloud computingEdgeInternet of things (IoT)DescriptorsMicroservicesOrchestrationDeploymentDigital twin (DT)Industryjournal article