Résumé
Scientific workflows are used to model experiments that rely on computer simulations. Because these workflows are typically data‐intensive, they commonly require execution in distributed environments. The cloud, with on‐demand and elastic resources, has emerged as a cost‐effective environment for workflow execution. However, the efficiency and cost‐effectiveness of cloud workflow execution depend on how the workflow is executed on these resources. In particular, reusing cached data to avoid re‐executing parts of the workflow is critical for performance but hard to make effective. This manuscript introduces MemoirGRASP , a workflow execution strategy based on the GRASP metaheuristic that optimizes execution while exploring memoization. The memoization technique involves caching intermediate results, reducing workflow execution redundancy, and enhancing efficiency across multiple workflow executions. The experimental evaluation of MemoirGRASP using synthetic workflows and two real‐world workflows demonstrates the advantages and benefits of the MemoirGRASP strategy in improving workflow execution efficiency.