When executing scientific workflows, anomalies of the workflow behavior are often caused by different issues such as resource failures at the underlying infrastructure. The provenance information collected by workflow management systems only captures the transformation of data at the workflow level. Analyzing provenance information and apposite system metrics requires expertise and manual effort. Moreover, it is often timeconsuming to aggregate this information and correlate events occurring at different levels of the infrastructure. In this paper, we propose an architecture to automate the integration among workflow provenance information and performance information from the infrastructure level. Our architecture enables workflow developers or domain scientists to effectively browse workflow execution information together with the system metrics, and analyze contextual information for possible anomalies.