Challenges and Potentials of Cloud-native Software Architectures Towards Industrial use in the Field of Computational Engineering

Abstract

Today, the productive implementation of computational fluid dynamics introduces many layers of complexity to the product development process e.g., large recurring hardware cost due to high performance computing demands, software maintenance routines and a strong data storage and administration strategies to manage the large, partially unstructured and diverse sets of data to archive and evaluate. In the last years, large cloud infrastructure providers further developed and promoted their services in the field of computer-aided engineering (CAE). The present work wants to give an overview of the most important technological advances in the field of cloud-native software architectures including the potential for more productive cloud-based engineering toolchains. First, the modern web- and cloud-based services are discussed from a software architecture perspective and evaluated towards the added value they potentially provide to the field of computational engineering. Here, the focus lies on popular topics like high performance computing in the cloud, centralized large data and microservice architecture, as well as how they can potentially contribute to the scalability and robustness of high demand services. Furthermore, the challenges and potential solutions in client-server communication incl. user authentication, management and transfer of large data sets, network latency and job prioritization are explained. In this context, the issue of data security is discussed and a holistic view on the data transfer from a client to a server, incl. potential vulnerabilities, is provided. Lastly, the introduction of a cloud-native infrastructure comes with the necessity of a fundamental business model refactoring. Thus, the cost structure and software-as-a-service (SaaS) business model behind an exemplary cloud-native CAE environment is presented based on existing industrial examples. This includes the overall value proposition, technological demands and general differentiation that a cloud-native CAE tool provides compared to a conventional on-premise software.