Service Oriented Architectures (SOA) are based on applications consisting of an aggregation of services with standard interface, offered on distributed hosts. The highly distributed nature and the load sensitivity of these architectures make it very difficult to guarantee performance requirements under rapidly-changing load conditions. This paper deals with the development of service oriented predictive autonomic systems that are capable to optimize themselves using a feedforward approach, by exploiting automatically generated performance predictions. The MAWeS (MetaPL/HeSSE Autonomic Web Services) framework allows the development of self-tuning applications that proactively optimize themselves by simulating the execution environment. An example of application development in MAWeS is thoroughly dealt with, showing the implementation of a system that exploits MAWeS services to choose dynamically among several different algorithms to meet response time constraints.