Workflow adaptation as an Autonomic Computing problem

The performance of long running scientific workflows stands to benefit from adapting to changes in their environment. Autonomic Computing provides methodologies for managing run-time adaptations in managed systems. In this paper, we apply the monitoring, analysis, planning and execution (MAPE) model from autonomic computing to support the runtime modification of workflows with the aim of improving their performance. We systematically identify run-time adaptations and indicate how such behaviours can be captured using the MAPE model from the Autonomic Computing community. By characterising these as autonomic computing problems we make a proposal about how workflow adaptation can be achieved.