Autonomic Service Oriented Architectures with MAWeS

The highly distributed nature and the load sensitivity of Service Oriented Architectures (SOA) make it very difficult to guarantee performance requirements under rapidlychanging load conditions. This paper deals with the development of service oriented 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. After a discussion on the possible design choices for the development of autonomic web services applications, a soft real-time test application is presented and the performance results obtained in three different execution scenarios are commented.