Economy-based Greedy Bidding for Resources for CAE Workflows in Hybrid Cloud Infrastructure

The advent of generative design in the automotive sector, characterised by the automatic and iterative exploration of expansive solution spaces to discover optimal design configurations, has significantly increased the demand for computational resources to run intensive computer-aided engineering (CAE) simulations within constrained time frames. The inherent limitations of static high-performance computing (HPC) clusters have necessitated the adoption of cloud resources due to their flexible and elastic nature, thereby enhancing the capacity to accommodate the computational demands of these iterative workflows. These workflows, represented as Directed Acyclic Graphs (DAGs), involve the serial and parallel execution of tasks, which can dynamically share resources with other workflows during idle periods. In this paper, we propose an economy-based approach to exploit the gaps generated by these idle periods through a bidding system, thereby enabling more efficient resource utilisation and reducing the average wait time, makespan, cost and deadline miss by more than 40%, 6%, 13% and 45%respectively against certain infrastructures and baselines. Furthermore, we explore the potential for generating revenue by renting out idle resources in a hybrid cloud setup. This approach not only aims to optimise the use of computational resources but also seeks to provide cost-effective solutions to meet the escalating demands of generative design in the automotive sector.