Adaptive and reflective middleware for the cloudification of simulation & optimization workflows

Abstract

The simulation and optimization of complex engineering designs in automotive or aerospace involves multiple mathematical tools, long-running workflows and resource-intensive computations on distributed infrastructures. Finding the optimal deployment in terms of task distribution, parallelization, collocation and resource assignment for each execution is a step-wise process involving both human input with domain-specific knowledge about the tools as well as the acquisition of new knowledge based on the actual execution history. In this paper, we present motivating scenarios as well as an architecture for adaptive and reflective middleware that supports smart cloud-based deployment and execution of engineering workflows. This middleware supports deep inspection of the workflow task structure and execution, as well as of the very specific mathematical tools, their executions and used parameters. The reflective capabilities are based on multiple meta-models to reflect workflow structure, deployment, execution and resources. Adaptive deployment is driven by both human input as meta-data annotations as well as the actual execution history of the workflows.