Summary form only given. Predictive performance models of e-Commerce applications allows grid workload managers to provide e-Commerce clients with qualities of service (QoS) whilst making efficient use of resources. We demonstrate the use of two 'coarse-grained' modelling approaches (based on layered queuing modelling and historical performance data analysis) for predicting the performance of dynamic e-Commerce systems on heterogeneous servers. Results for a popular e-Commerce benchmark show how request response times and server throughputs can be predicted on servers with heterogeneous CPUs at different background loads. The two approaches are compared and their usefulness to grid workload management is considered.