Dissertation (August 11, 2026): André Igor Pereira

Student: André Igor Pereira

Title: AranduFlow: Towards Using Performance and Domain Metadata to Predict workflows execution time in HPC Environments

Advisors: Rafaelli Coutinho (Advisor) and Daniel de Oliveira (Co-advisor)

Committee: Rafaelli Coutinho (Cefet/RJ), Daniel de Oliveira (IC/UFF),  Eduardo Ogasawara (Cefet/RJ), Victor Stroele de Andrade Menezes (UFJF)

Day/hour:  August 11, 2026 / 2 p.m

Room: https://teams.microsoft.com/meet/218267218561286?p=zCgPEPHZ496sanbuRF

Abstract: The increasing complexity of scientific experiments has expanded the use of workflows to organize applications executed in High-Performance Computing (HPC) environments. In these environments, predicting execution time in advance is challenging because of variations in input data, application parameters, and computing infrastructure configurations. Furthermore, resource contention in shared environments can cause similar executions to have different durations. To assist users with workflow submission and experiment planning, this dissertation presents AranduFlow, an adaptable science gateway that uses historical data and machine learning models to estimate execution-time ranges. The proposed approach integrates submission, metadata collection, and prediction mechanisms by combining input-data characteristics, domain-specific parameters, and computing infrastructure information. Although the architecture can be adapted to different applications, the predictive models are trained specifically for each workflow. The approach was evaluated using two real-world scientific workflows from the bioinformatics and astronomy domains, with distinct resource-utilization profiles, executed on heterogeneous HPC infrastructures. The experiments showed that tree-based models outperformed the reference method and achieved accuracies above 80% in scenarios using broader execution-time ranges. The results also indicated the relevance of domain metadata and workload characteristics to the predictions. When applied to a new computing infrastructure, calibration using a small set of local executions produced a substantial improvement in predictive performance. These results provide evidence that \texttt{AranduFlow} can support execution-time estimation for heterogeneous workflows and can be adapted to different computing environments.