Scientific workflows execute on a loosely connected set of distributed and heterogeneous computational resources. The Integrated End-to-End Performance Prediction and Diagnosis (IPPD) project contributes to clear understanding of the factors that influence the performance and potential optimization of scientific workflows. IPPD addressed three core issues in order to provide insights into workflow execution that can be used to both explain and optimize their execution:

• provide an expectation of the performance of a workflow in-advance of execution to provide a best baseline performance
• identify areas of consistent low performance and diagnose the reason why
• study the important issue of performance variability

The design and analysis of large-scale scientific workflows is difficult precisely because each task can exhibit extreme performance variability. New prediction and diagnostic methods are required to enable efficient use of present and emerging workflow resources.