DAG- FGL: An Enhanced Approach for Accurate Workflow Task Execution Time Prediction with Complex Dependencies

We propose a new workflow task execution time prediction approach, DAG-FGL, by integrating Flash attention mechanism with a GraphLSTM model. It addresses the challenge of low task execution time prediction accuracy in the presence of complex dependencies among workflow subtasks. The GraphLSTM model captures and conveys subtask dependencies through the adjacency matrix of sub task relationships modeled as a directed acyclic graph (DAG). The Flash attention mechanism enhances the model by incorporating customized positional encoding of subtask priority. The encoding ensures that the model accurately reflects each subtask's importance and relative order when calculating attention weight. Therefore, DAG- FG L can more accurately predict task execution time in the context of complex dependencies. Experimental results show that DAG-FGL outperforms the best-performing baseline model. It achieves 7.82 % to 44.52 % improvements in prediction accuracies over the best-performing baselines across three cloud workflow datasets of varying lengths.