Autonomic Composite-service Architecture with MAWeS

The highly distributed nature and the load sensitivity of Service Oriented Architectures (SOA) make it very difficult to guarantee performance requirements under rapidly-changing load conditions. This paper deals with the development of service oriented autonomic systems that are capable to optimize themselves using a feed forward 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 a composite-service execution scenario are commented.