A Cloud-native Implementation of the Simulation as a Service-Concept Based on FMI

Abstract

Providing modelling and simulation capabilities as a service promises to increase their value by improving accessibility for non-expert users and software agents as well as by leveraging cloud-computing technology to scale simulation performance beyond the capabilities of a single computer. In order to reach this potential, implementations must align their design with the architectural styles of cloud computing applications and the web in general.We present an open-source, cloud-native Simulation as a Service (SIMaaS)-implementation that gives access to models and allows simulating them on the web. The implementation uses Functional Mockup Units (FMUs) for co-simulation as an executable form of a model and relies on FMPy for simulation. It is realized as a microservice in the form of a REST-based HTTP-API. Functionality and performance are demonstrated by using the service to create ensemble forecasts for PV systems and to search for an optimal parameter set using a genetic algorithm. Conceptual limitations and the resulting opportunities for further work are summarized.

Dynamic system models and their simulation in the Semantic Web

“…From a non-functional perspective, the service should be designed as a Cloud-native Application (CNA) [20] and, consequently, as a Cloud-native Simulation (CNS) system [21, p. 15] because not designing it as a CNA would mean to disregard the expected and proven characteristics for Software as a Service (SaaS) and the corresponding best practices. Consequently, the software should be realized as a microservice, isolate the state in a single component, use containers as deployment units and follow best practices for the development of SaaS (for details see [35]).…”

“…For the second hypothesis, machine-actionability will be demonstrated through the API's use by the PPAimplementation as a software that was not specifically programmed to use it. FAIRness and support for loose coupling will be evaluated by comparing the developed hypermedia API to its non-RESTful predecessor (detailed in [35]) for each of the 15 FAIR principles and for each of the coupling facets identified by Pautasso and Wilde [49], respectively.…”

“…The M&S hypermedia API is an evolution of the "Cloud-native Implementation of the Simulation as a Service-Concept Based on FMI", which was presented at the Modelica conference 2021 [35]. This earlier version was a REST-based HTTP-API that used JSON-serializations for resource representations.…”

“…It did not support HATEOAS and required programmers to code clients against the OpenAPI Specification, which was regenerated for every model added. The article [35] details concepts, implementation principles and limitations alongside the presentation of two exemplary applications and a discussion of related work in the Modelica community.…”