The efficient execution of integration processes between distributed, heterogeneous data sources and applications is a challenging research area of data management. These integration processes are an abstraction for work ow-based integration tasks, used in EAI servers and WfMS. The major problem are significant workload changes during run-time. The performance of integration processes strongly depends on those dynamic workload characteristics, and hence workload-based optimization is important. However, existing approaches of work ow optimization only address the rule-based optimization and disregard changing workload characteristics. To overcome the problem of ineficient process execution in the presence of workload shifts, here, we present an approach for the workload-based optimization of instance-based integration processes and show that significant execution time reductions are possible.